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Juvenile growth curves are generally sigmoid in shape: Growth is initially nearly exponential, but it slows to near zero as the animal approaches maturity. The drop-off in growth rate is puzzling because, everything else being equ...
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Juvenile growth curves are generally sigmoid in shape: Growth is initially nearly exponential, but it slows to near zero as the animal approaches maturity. The drop-off in growth rate is puzzling because, everything else being equal, selection favors growing as fast as possible. Existing theory posits sublinear scaling of resource acquisition with juvenile body mass and linear scaling of the requirement for maintenance, so the difference, fuel for growth, decreases as the juvenile increases in size. Experimental evidence, however, suggests that maintenance metabolism increases sublinearly not linearly with size. Here, we develop a new theory consistent with the experimental evidence. Our theory is based on the plausible assumption that there is a trade-off in the capacity of capillaries to supply growing and developed cells. As the proportion of non-growing cells increases, they take up more macromolecules from the capillaries, leaving fewer to support growing cells. The predicted growth curves are realistic and similar to those of previous models (Bertalanffy, Gompertz, and Logistic) but have the advantage of being derived from a plausible physiological model. We hope that our focus on resource delivery in capillaries will encourage new experimental work to identify the detailed physiological basis of the trade-off underlying juvenile growth curves.
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While numerous empirical studies include proxies for growth opportunities in their analyses, there is limited evidence as to the validity of the various growth proxies used. Based on a sample of 1942 firm-years for listed UK compa...
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While numerous empirical studies include proxies for growth opportunities in their analyses, there is limited evidence as to the validity of the various growth proxies used. Based on a sample of 1942 firm-years for listed UK companies over the 1990-2004 period, we assess the performance of eight growth opportunities measures. Our results show that while all the growth measures show some ability to predict growth in company sales, total assets, or equity, there are substantial differences between the various models. In particular, Tobin's Q performs poorly while dividend-based measures generally perform best. However, none of the measures has any success in predicting earnings per share growth, even when controlling for mean reversion and other time-series patterns in earnings. We term this the 'growth companies puzzle'. Growth companies do grow, but they do not grow in the key dimension (earnings) theory predicts. Whether the failure of 'growth companies' to deliver superior earnings growth is attributable to increased competition, poor investments, or behavioural biases, it is still a puzzle why growth companies on average fail to deliver superior earnings growth.
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The growth patterns of a child changes from uterine life until the end of puberty. Height velocity is highest in utero and declines after birth until puberty when it rises again. Important hormonal regulators of childhood growth a...
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The growth patterns of a child changes from uterine life until the end of puberty. Height velocity is highest in utero and declines after birth until puberty when it rises again. Important hormonal regulators of childhood growth are growth hormone, insulin-like growth factor 1, sex steroids, and thyroid hormone. This review gives an overview of these hormonal regulators of growth and their interplay with nutrition and other key players such as inflammatory cytokines. (C) 2017 S. Karger AG, Basel
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This article looks at the efficacy of a diverse set of local growth management programs undertaken in the United States since the early 1970s. Organized into three sections, it begins with a brief history of growth management mile...
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This article looks at the efficacy of a diverse set of local growth management programs undertaken in the United States since the early 1970s. Organized into three sections, it begins with a brief history of growth management milestones, tracing the evolution of growth management programs from Ramapo, New York?s original 1969 ordinance to the emergence of the Smart Growth movement in the mid-1990s. A second part organizes and summarizes the growth management efficacy and adverse effect literatures. A third part takes a fresh look at the success of local growth management programs by comparing population growth, sprawl, and fiscal and housing price outcome measures across eight pairs of communities, one of which (i.e., ?case study community?) adopted a growth management program, and the other (i.e., ?peer community?) which did not. It concludes with a summary assessment of fifty years of local growth management experiences, along with some lessons for how planners might best deal with forthcoming rounds of suburban growth.
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Intrusive growth is a type of cell elongation when the rate of its longitudinal growth is higher than that of surrounding cells; therefore, these cells intrude between the neighboring cells penetrating the middle lamella. The revi...
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Intrusive growth is a type of cell elongation when the rate of its longitudinal growth is higher than that of surrounding cells; therefore, these cells intrude between the neighboring cells penetrating the middle lamella. The review considers the classical example of intrusive growth, e.g., elongation of sclerenchyma fibers when the cells achieve the length of several centimeters. We sum the published results of investigations of plant fiber intrusive growth and present some features of intrusive growth characterized by the authors for flax (Linum usitatissimum L.) and hemp (Cannabis sativa L.) fibers. The following characteristics of intrusive growth are considered: its rate and duration, relationship with the growth rate of surrounding cells, the type of cell elongation, peculiarities of the fiber primary cell wall structure, fibers as multinucleate cells, and also the control of intrusive growth. Genes, which expression is sharply reduced at suppression of intrusive growth, are also considered. Arguments for separation of cell elongation and secondary cell wall formation in phloem fibers and also data indicating diffuse type of cell enlargement during intrusive growth are presented.
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The increase in shallot production can be achieved by mulching. Plastic mulch and plant residues that are commonly applied still have shortcomings, so innovation is needed through the formulation of organic mulch sheets (OMS). Thi...
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The increase in shallot production can be achieved by mulching. Plastic mulch and plant residues that are commonly applied still have shortcomings, so innovation is needed through the formulation of organic mulch sheets (OMS). This study aimed to examine the effect of the composition of OMS on the growth and biomass of shallot. The research was conducted from December 2019 to February 2020 at the UMM Experimental Field, Junrejo, Batu, East Java. Materials were shallot bulb and raw materials of OMS (water hyacinth, banana stem, and coconut husk). The experiment used a simple RCBD with 6 treatments, namely 1 control (without mulch) and 5 treatments with different OMS compositions (percentage of water hyacinth (EG), banana stem (PP), and coconut husk (SK)) i.e., M1 (40%EG: 40%PP: 20%SK), M2 (30%EG: 50%PP: 20%SK), M3 (50%EG: 40%PP: 10%SK), M4 (60%EG: 30%PP: 10%SK) dan M5 (40%EG: 50%PP: 10%SK). The growth and biomass data were analyzed using ANOVA, mean comparison using Tukey’s honestly significant difference (HSD) test), and the calculation of AGR and CGR values. The composition of OMS made from water hyacinth, banana stem, and coconut husk had a significant effect on plant height, number of leaves, stem diameter, and number of tillers at the end of observation. The OMS application increased the biomass (total fresh weight and dry weight) significantly at harvest. The OMS made of 40% water hyacinth, 40% banana stem, and 20% coconut husk showed higher growth and plant biomass as well as higher AGR and CGR values than other OMS treatments.
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Growth reference charts are the basic assessment tool of child health whether at an individual, community, or national level. The comparison of a child's height or weight with the distribution of heights or weights of a reference ...
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Growth reference charts are the basic assessment tool of child health whether at an individual, community, or national level. The comparison of a child's height or weight with the distribution of heights or weights of a reference sample provides evidence of the normality or otherwise of the process of growth. In the UK, the most widely used growth references are the UK1990 growth references (UK9O). In 2006 the World Health Organisation launched the new chart and stated that, "The new growth curves are expected to provide a single international standard that represents the best description of physiological growth for all children from birth to five years of age and to establish the breastfed infant as the normative model for growth and development." It is important to distinguish between a growth reference (e.g. UK90) and a growth standard (e.g. WHO 2006). Growth reference charts are usually based on cross-sectional data, they are for use with samples and they reflect growth "as is" rather than "as it ought to be". Growth standards are a different tool - they are based on longitudinal data in which the source sample has been selected according to some pre-defined criteria and reflect growth not "as is" but growth "as it ought to be". The Scientific Advisory Committee on Nutrition (SACN) recommended in 2007 that a modified WHO chart be adopted in the UK. These UK-WHO growth charts combined the UK90 data with the. WHO data; the WHO data were used from birth to 4 years and the UK90 data from 4 years onwards. However, the WHO charts are standards not references and reflect the growth of children growing in unconstrained environments. Because they are longitudinal standards the WHO charts (particularly from birth to 2 years) should be used specifically for individuals rather than samples. They do not purport to represent the growth characteristics of the average child in a population and if used as a growth reference rather than a growth standard they will potentially provide conflicting and erroneous information. Tests of the UK-WHO chart in the UK indicate a good fit for length/height but a relatively poorer fit for weight because of slower weight gain in the WHO sample. Tests of the WHO charts from outside the UK recognise their usefulness for well-fed economically privileged children but recommend local growth charts when available. In addition, the vast majority of UK infants are not exclusively breastfed for anywhere near the recommended duration of 4 months and the duration of breastfeeding is heavily influenced by social and economic circumstances. It is, of course, the infants who live in social and economically deprived conditions, with young, poorly educated mothers who also have the greatest risk of growth faltering and for whom an accurate and appropriate growth assessment tool is of paramount importance.The choice of a growth chart clearly depends on the question being asked. Growth charts are not only tools but they are very specific tools designed to do a particular job. Their appropriate use requires training and education even though their appearance as relatively simple graphs of height or weight against age gives the impression of simplicity and a lack of sophistication. Indeed they are neither of these things. If the question relates to the growth of a child in comparison to breastfed infants living in socio-economic conditions that do not constrain growth then the WHO standard is the appropriate tool. If, on the other hand, the question relates to the growth of a child in comparison to other British children from no specific socio-economic background and with no specific feeding regime, then the UK90 will effectively answer the question. Of course, the WHO standard can be used in both situations but the probable absence of exclusive breastfeeding and perhaps the variation in socio-economic conditions within which the infant lives will necessarily alter the interpretation of the pattern of growth.
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Background This study investigated the relationship between fibroblast growth factor 21 (FGF21) levels and growth in children with growth hormone deficiency (GHD) and idiopathic short stature (ISS), and the effects of the FGF21 le...
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Background This study investigated the relationship between fibroblast growth factor 21 (FGF21) levels and growth in children with growth hormone deficiency (GHD) and idiopathic short stature (ISS), and the effects of the FGF21 level on response to growth hormone (GH) treatment. Methods We included 171 pre-pubertal children with a GHD (n = 54), ISS (n = 46), and normal height (n = 71). Fasting FGF21 levels were measured at baseline and every 6 months during GH treatment. Factors associated with growth velocity (GV) after GH therapy were investigated. Results The FGF21 level was higher in short children than in the controls without significant difference between the GHD and ISS groups. In the GHD group, the FGF21 level was inversely associated with the free fatty acid (FFA) level at baseline ( r = ?0.28, P = 0.039), however, was positively correlated with the FFA level at 12 months ( r = 0.62, P = 0.016). The GV over 12 months of GH therapy was positively associated with the delta insulin-like growth factor 1 level (β = 0.003, P = 0.020). The baseline log-transformed FGF21 level was inversely associated with GV with marginal significance (β = ?0.64, P = 0.070). Conclusion The FGF21 level was higher in children of short stature, both those with GHD and the ISS, than in children with normal growth. The pretreatment FGF21 level negatively affected the GV of children with GH-treated GHD. These results suggest the existence of a GH/FFA/FGF21 axis in children.
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Summary Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the producti...
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Summary Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question ‘what is growth?’. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.
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The underlying sources of growth variability in a population cannot generally be known, so when modelling growth it is important to understand the consequences of assuming an incorrect error structure. In this study, four error mo...
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The underlying sources of growth variability in a population cannot generally be known, so when modelling growth it is important to understand the consequences of assuming an incorrect error structure. In this study, four error models for a von Bertalanffy growth curve with asymptotic length parameter L sub([infinity]) and growth rate parameter k are considered. Simulations are carried out in which data are generated according to one of the models and fitted assuming each of the models to be true. This is done for two types of data: direct age-length and tag-recapture. For direct age- length data, the consequences of not accounting for individual growth variability, or assuming the wrong source of variability, are minor, even when individual variability is high or data coverage is poor. For tag- recapture data, some substantial biases in growth estimates can arise when individual variability exists but is not accounted for. Importantly, however, incorporating variability in just one parameter (be it L sub([infinity]) or k), even if the variability truly stems from the other or both parameters, generally leads to much smaller biases than assuming no individual variability. Often the alternative models cannot be distinguished using standard model selection procedures, so caution is warranted in using model selection to draw inferences about underlying sources of growth variability.
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