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Oxygen consumption (VO2), carbon dioxide production (VCO2), the resulting respiratory quotient (RQ), and motor activity were recorded simultaneously by an on-line computer every ten seconds during 16-20 hours in two decerebrate male rats. Being aphagic and adipsic the rats were fed twice daily by gastric intubation with a mixture of powdered milk plus sugar or plus sunflower oil (approx. 300 KJ daily) in 10-20 ml tap water. In all seven tests performed on these rats the recordings presented very steep reductions of RQ due every time to steep increases in VO2 without increases in VCO2. Mean number of VO2 peaks in all experiments was 12.4 +/- 1.8 (SE) with mean duration of 21.3 +/- 2.8 min. Two normal male rats were fed the same diet and on the same schedule: they presented similar VO2 peaks in 8 out of 12 experiments. Mean number was 8.7 +/- 1.0 with mean duration of 13.6 +/- 2.2 min. The VO2 peak periods never occurred in rats fed ad libitum. In the two normal rats oil ingestion produced more effect than sugar. It is suggested that the phenomenon could be due to a metabolic imbalance possibly of hepatic origin, more evident in decerebrate rats. VO2 peaks could be produced by enhanced ketogenesis, gluconeogenesis and/or extra-mitochondrial (peroxisomal, microsomal) oxidation. 相似文献
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Species–area curves from islands and other isolates often differ in shape from sample‐area curves generated from mainlands or sections of isolates (or islands), especially at finer scales. We examine two explanations for this difference: (1) the small‐island effect (SIE), which assumes the species–area curve is composed of two distinctly different curve patterns; and (2) a sigmoid or depressed isolate species–area curve with no break‐points (in arithmetic space). We argue that the application of Ockham’s razor – the principle that the simplest, most economical explanation for a hypothesis should be accepted over less parsimonious alternatives – leads to the conclusion that the latter explanation is preferable. We hold that there is no reason to assume the ecological factors or patterns that affect the shapes of isolate (or island) curves cause two distinctly different patterns. This assumption is not required for the alternative, namely that these factors cause a single (though depressed) isolate species–area curve with no break‐points. We conclude that the theory of the small‐island effect, despite its present standing as an accepted general pattern in nature, should be abandoned. 相似文献
5.
Growth of the young is an important part of the life history in birds. However, modelling methods have paid little attention to the choice of regression model used to describe its pattern. The aim of this study was to evaluate whether a single sigmoid model with an upper asymptote could describe avian growth adequately. We compared unified versions of five growth models of the Richards family (the four‐parameter U‐Richards and the three‐parameter U‐logistic, U‐Gompertz, U‐Bertalanffy and U4‐models) for three traits (body mass, tarsus‐length and wing‐length) for 50 passerine species, including species with varied morphologies and life histories. The U‐family models exhibit a unified set of parameters for all models. The four‐parameter U‐Richards model proved a good choice for fitting growth curves to various traits – its extra d‐parameter allows for a flexible placement of the inflection point. Which of the three‐parameter U‐models was the best performing varied greatly between species and between traits, as each three‐parameter model had a different fixed relative inflection value (fraction of the upper asymptote), implying a different growth pattern. Fixing the asymptotes to averages for adult trait value generally shifted the model preference towards one with lower relative inflection values. Our results illustrate an overlooked difficulty in the analysis of organismal growth, namely, that a single traditional three‐parameter model does not suit all growth data. This is mostly due to differences in inflection placement. Moreover, some biometric traits require more attention when estimating growth rates and other growth‐curve characteristics. We recommend fitting either several three‐parameter models from the U‐family, where the parameters are comparable between models, or only the U‐Richards model. 相似文献
6.
Even Tjørve 《Journal of Biogeography》2009,36(8):1435-1445
This paper has extended and updated my earlier list and analysis of candidate models used in theoretical modelling and empirical examination of species–area relationships (SARs). I have also reviewed trivariate models that can be applied to include a second independent variable (in addition to area) and discussed extensively the justifications for fitting curves to SARs and the choice of model. There is also a summary of the characteristics of several new candidate models, especially extended power models, logarithmic models and parameterizations of the negative-exponential family and the logistic family. I have, moreover, examined the characteristics and shapes of trivariate linear, logarithmic and power models, including combination variables and interaction terms. The choice of models according to best fit may conflict with problems of non-normality or heteroscedasticity. The need to compare parameter estimates between data sets should also affect model choice. With few data points and large scatter, models with few parameters are often preferable. With narrow-scale windows, even inflexible models such as the power model and the logarithmic model may produce good fits, whereas with wider-scale windows where inflexible models do not fit well, more flexible models such as the second persistence (P2) model and the cumulative Weibull distribution may be preferable. When extrapolations and expected shapes are important, one should consider models with expected shapes, e.g. the power model for sample area curves and the P2 model for isolate curves. The choice of trivariate models poses special challenges, which one can more effectively evaluate by inspecting graphical plots. 相似文献
7.
Stina K. Lien Håvard Sletta Trond E. Ellingsen Svein Valla Elon Correa Royston Goodacre Kai Vernstad Sven Even Finborud Borgos Per Bruheim 《Metabolomics : Official journal of the Metabolomic Society》2013,9(2):403-417
Metabolic profiling of Pseudomonas fluorescens SBW25 and various mutants derived thereof was performed to explore how the bacterium adapt to changes in carbon source and upon induction of alginate synthesis. The experiments were performed at steady-state conditions in nitrogen-limited chemostats using either fructose or glycerol as carbon source. Carbon source consumption was up-regulated in the alginate producing mutant with inactivated anti-sigma factor MucA. The mucA- mutants (also non-alginate producing mucA- control strains) had a higher dry weight yield on carbon source implying a change in carbon and energy metabolism due to the inactivation of the anti-sigma factor MucA. Both LC–MS/MS and GC–MS methods were used for quantitative metabolic profiling, and major reorganization of primary metabolite pools in both an alginate producing and a carbon source dependent manner was observed. Generally, larger changes were observed among the phosphorylated glycolytic metabolites, the pentose phosphate pathway metabolites and the nucleotide pool than among amino acids and citric acid cycle compounds. The most significant observation at the metabolite level was the significantly reduced energy charge of the mucA- mutants (both alginate producing and non-producing control strains) compared to the wild type strain. This reduction was caused more by a strong increase in the AMP pool than changes in the ATP and ADP pools. The alginate-producing mucA- mutant had a slightly increased GTP pool, while the GDP and GMP pools were strongly increased compared to non-producing mucA- strains and to the wild type. Thus, whilst changes in the adenosine phosphate nucleotide pool are attributed to the mucA inactivation, adjustments in the guanosine phosphate nucleotide pool are consequences of the GTP-dependent alginate production induced by the mucA inactivation. This metabolic profiling study provides new insight into carbon and energy metabolism of the alginate producer P. fluorescens. 相似文献
8.
Joshua?C?KwekelEmail author Varsha?G?Desai Carrie?L?Moland Vikrant?Vijay James?C?FuscoeEmail author 《Biology of sex differences》2013,4(1):14
Background
The kidney functions in key physiological processes to filter blood and regulate blood pressure via key molecular transporters and ion channels. Sex-specific differences have been observed in renal disease incidence and progression, as well as acute kidney injury in response to certain drugs. Although advances have been made in characterizing the molecular components involved in various kidney functions, the molecular mechanisms responsible for sex differences are not well understood. We hypothesized that the basal expression levels of genes involved in various kidney functions throughout the life cycle will influence sex-specific susceptibilities to adverse renal events.Methods
Whole genome microarray gene expression analysis was performed on kidney samples collected from untreated male and female Fischer 344 (F344) rats at eight age groups between 2 and 104 weeks of age.Results
A combined filtering approach using statistical (ANOVA or pairwise t test, FDR 0.05) and fold-change criteria (>1.5 relative fold change) was used to identify 7,447 unique differentially expressed genes (DEGs). Principal component analysis (PCA) of the 7,447 DEGs revealed sex-related differences in mRNA expression at early (2 weeks), middle (8, 15, and 21 weeks), and late (104 weeks) ages in the rat life cycle. Functional analysis (Ingenuity Pathway Analysis) of these sex-different genes indicated over-representation of specific pathways and networks including renal tubule injury, drug metabolism, and immune cell and inflammatory responses. The mRNAs that code for the qualified urinary protein kidney biomarkers KIM-1, Clu, Tff3, and Lcn2 were also observed to show sex differences.Conclusions
These data represent one of the most comprehensive in-life time course studies to be published, assessing sex differences in global gene expression in the F344 rat kidney. PCA and Venn analyses reveal specific periods of sexually dimorphic gene expression which are associated with functional categories (xenobiotic metabolism and immune cell and inflammatory responses) of key relevance to acute kidney injury and chronic kidney disease, which may underlie sex-specific susceptibility. Analysis of the basal gene expression patterns of renal genes throughout the life cycle of the rat will improve the use of current and future renal biomarkers and inform our assessments of kidney injury and disease.9.
The perinucleolar compartment (PNC) is a subnuclear stucture forming predominantly in cancer cells; its prevalence positively correlates with metastatic capacity. Although several RNA-binding proteins have been characterized in PNC, the molecular function of this compartment remains unclear. Here we demonstrate that the cyclin–dependent kinase 13 (CDK13) is a newly identified constituent of PNC. CDK13 is a kinase involved in the regulation of gene expression and whose overexpression was found to alter pre-mRNA processing. In this study we show that CDK13 is enriched in PNC and co-localizes all along the cell cycle with the PNC component PTB. In contrast, neither the cyclins K and L, known to associate with CDK13, nor the potential kinase substrates accumulate in PNC. We further show that CDK13 overexpression increases PNC prevalence suggesting that CDK13 may be determinant for PNC formation. This result linked to the finding that CDK13 gene is amplified in different types of cancer indicate that this kinase can contribute to cancer development in human. 相似文献
10.
Anne-Marie Lundsgaard Jacob B. Holm Kim A. Sjøberg Kirstine N. Bojsen-Møller Lene S. Myrmel Even Fjære Benjamin A.H. Jensen Trine S. Nicolaisen Janne R. Hingst Sine L. Hansen Sophia Doll Philip E. Geyer Atul S. Deshmukh Jens J. Holst Lise Madsen Karsten Kristiansen Jørgen F.P. Wojtaszewski Erik A. Richter Bente Kiens 《Cell metabolism》2019,29(1):50-63.e4