Analyzing stochastic transcription to elucidate the nucleoid's organization-0

curve) when the plasmid is actively transcribed (and translated; data set A). The red curve shows the autocorrelation function when the genes' positions were randomly assigned. The Y-axis has been cropped at an autocorrelation of 0.15 for a clearer visual interpretation; the blue curve starts at an autocorrelation of 0.27 for a gene distance of one. Maxima (positive correlation) and minima (negative or anti-correlation) can be clearly distinguished, with a strong anti-correlation for genes that lie opposite of each other on the chromosome (a gene distance of around 650). Similarly, Figures 1b and 1c show the results for the chromosome and the megaplasmid pSymB, respectively, when both are actively transcribed (and translated; data set A). As can be seen, the autocorrelation functions for the three replicons are similar. Note: The figures are all at the same scale to better illustrate the different sizes of the three replicons. All Y-axes have been cropped at a value of 0.15 for (visual) clarity's sake.<p><b>Copyright information:</b></p><p>Taken from "Analyzing stochastic transcription to elucidate the nucleoid's organization"</p><p>http://www.biomedcentral.com/1471-2164/9/125</p><p>BMC Genomics 2008;9():125-125.</p><p>Published online 10 Mar 2008</p><p>PMCID:PMC2270832.</p><p></p

Analyzing stochastic transcription to elucidate the nucleoid's organization-1

curve) when only stochastic transcription takes place (data set B). The red curve shows the autocorrelation function when the genes' positions were randomly assigned. The Y-axis has been cropped at an autocorrelation of 0.15 for a clearer visual interpretation; the blue curve starts at an autocorrelation of 0.15 for a gene distance of one. The signal becomes quickly confounded with the noise (red curve). There are minima and maxima that stand out, but only a spectral analysis can tell, whether these are significant or not. Again to serve as comparison, Figures 2b and 2c show the autocorrelation functions for the chromosome and the megaplasmid psymB, respectively, for data set B. Both replicons are actively transcribed and translated, unlike psymA, and their autocorrelation functions are comparable to those in data set A (as confirmed by the spectral and statistical analyses, see additional file ). Note: The figures are all at the same scale to better illustrate the different sizes of the three replicons. All Y-axes have been cropped at a value of 0.15 for (visual) clarity's sake.<p><b>Copyright information:</b></p><p>Taken from "Analyzing stochastic transcription to elucidate the nucleoid's organization"</p><p>http://www.biomedcentral.com/1471-2164/9/125</p><p>BMC Genomics 2008;9():125-125.</p><p>Published online 10 Mar 2008</p><p>PMCID:PMC2270832.</p><p></p

Analyzing stochastic transcription to elucidate the nucleoid's organization-2

curve) when the plasmid is actively transcribed (and translated; data set A). The red curve shows the autocorrelation function when the genes' positions were randomly assigned. The Y-axis has been cropped at an autocorrelation of 0.15 for a clearer visual interpretation; the blue curve starts at an autocorrelation of 0.27 for a gene distance of one. Maxima (positive correlation) and minima (negative or anti-correlation) can be clearly distinguished, with a strong anti-correlation for genes that lie opposite of each other on the chromosome (a gene distance of around 650). Similarly, Figures 1b and 1c show the results for the chromosome and the megaplasmid pSymB, respectively, when both are actively transcribed (and translated; data set A). As can be seen, the autocorrelation functions for the three replicons are similar. Note: The figures are all at the same scale to better illustrate the different sizes of the three replicons. All Y-axes have been cropped at a value of 0.15 for (visual) clarity's sake.<p><b>Copyright information:</b></p><p>Taken from "Analyzing stochastic transcription to elucidate the nucleoid's organization"</p><p>http://www.biomedcentral.com/1471-2164/9/125</p><p>BMC Genomics 2008;9():125-125.</p><p>Published online 10 Mar 2008</p><p>PMCID:PMC2270832.</p><p></p

Datasheet1_Impact of dobutamine stress on diastolic energetic efficiency of healthy left ventricle: an in vivo kinetic energy analysis.pdf

The total kinetic energy (KE) of blood can be decomposed into mean KE (MKE) and turbulent KE (TKE), which are associated with the phase-averaged fluid velocity field and the instantaneous velocity fluctuations, respectively. The aim of this study was to explore the effects of pharmacologically induced stress on MKE and TKE in the left ventricle (LV) in a cohort of healthy volunteers. 4D Flow MRI data were acquired in eleven subjects at rest and after dobutamine infusion, at a heart rate that was ∼60% higher than the one in rest conditions. MKE and TKE were computed as volume integrals over the whole LV and as data mapped to functional LV flow components, i.e., direct flow, retained inflow, delayed ejection flow and residual volume. Diastolic MKE and TKE increased under stress, in particular at peak early filling and peak atrial contraction. Augmented LV inotropy and cardiac frequency also caused an increase in direct flow and retained inflow MKE and TKE. However, the TKE/KE ratio remained comparable between rest and stress conditions, suggesting that LV intracavitary fluid dynamics can adapt to stress conditions without altering the TKE to KE balance of the normal left ventricle at rest.</p

Microbiota in anorexia nervosa: The triangle between bacterial species, metabolites and psychological tests

<div><p>Anorexia nervosa (AN) is a psychiatric disease with devastating physical consequences, with a pathophysiological mechanism still to be elucidated. Metagenomic studies on anorexia nervosa have revealed profound gut microbiome perturbations as a possible environmental factor involved in the disease. In this study we performed a comprehensive analysis integrating data on gut microbiota with clinical, anthropometric and psychological traits to gain new insight in the pathophysiology of AN. Fifteen AN women were compared with fifteen age-, sex- and ethnicity-matched healthy controls. AN diet was characterized by a significant lower energy intake, but macronutrient analysis highlighted a restriction only in fats and carbohydrates consumption. Next generation sequencing showed that AN intestinal microbiota was significantly affected at every taxonomic level, showing a significant increase of <i>Enterobacteriaceae</i>, and of the archeon <i>Methanobrevibacter smithii</i> compared with healthy controls. On the contrary, the genera <i>Roseburia</i>, <i>Ruminococcus</i> and <i>Clostridium</i>, were depleted, in line with the observed reduction in AN of total short chain fatty acids, butyrate, and propionate. Butyrate concentrations inversely correlated with anxiety levels, whereas propionate directly correlated with insulin levels and with the relative abundance of <i>Roseburia inulinivorans</i>, a known propionate producer. BMI represented the best predictive value for gut dysbiosis and metabolic alterations, showing a negative correlation with <i>Bacteroides uniformis</i> (microbiota), with alanine aminotransferase (liver function), and with psychopathological scores (obsession-compulsion, anxiety, and depression), and a positive correlation with white blood cells count. In conclusion, our findings corroborate the hypothesis that the gut dysbiosis could take part in the AN neurobiology, in particular in sustaining the persistence of alterations that eventually result in relapses after renourishment and psychological therapy, but causality still needs to be proven.</p></div

Fecal SCFA concentrations in anorexia nervosa subjects and healthy control group.

<p>Fecal SCFA concentrations in anorexia nervosa subjects and healthy control group.</p

Taxa significantly increased (+) or decreased (-) in abundance in anorexia nervosa subjects.

<p>Taxa significantly increased (+) or decreased (-) in abundance in anorexia nervosa subjects.</p

Raw scores for psychopathological assessment.

<p>Raw scores for psychopathological assessment.</p

Distance-based redundancy analysis (db-RDA).

<p>Db-RDA plot shows correlations between gut microbiota composition and body mass index (BMI), insulin, propionate and butyrate. Only significant variables are represented (p>0.05, Permanova analysis). Arrows in the db-RDA biplot denote the magnitudes and directions of the variable effects. Controls are represented by blue dots and anorexic subjects by red dots.</p

Characteristics of the study population.

<p>Characteristics of the study population.</p