Lactate has been reconsidered! As we now know, most is produced aerobically We report that lactate accumulation commonly occurs in the presence of oxygen and is sufficient to instigate signals for angiogenesis and connective tissue deposition. These include vascular endothelial growth factor (VEGF), transforming growth factor beta (TGF beta), interleukin-1 (IL-1), and hypoxia-inducible factor (hif-1alpha). This paper, a mini-review, is occasioned by new data showing increased presence of VEGF and angiogenesis in an oxygenated site by adding a slow-release source of lactate into Matrigel and implanting the Matrigel subcutaneously in mice.
L-moments based regional flood frequency analysis has been carried out on the seven sites of Punjab, Pakistan. Discordancy measure in terms of L-moments has been used to screen the data on each of the seven sites. Homogeneity of the region has been tested using the L-moments based heterogeneity measure (H). H has been calculated using four parameter Kappa distribution with 500 simulations. In order to find the most suitable distribution for quantile estimates, a number of L-moments based frequency distributions, such as, generalized logistic (GLO), generalized extreme-value (GEV), generalized normal (GNO), Pearson type III (PE3), generalized Pareto (GPA) and five parameter Wakeby (WAK) distribution, have been used. Based on the L-moment ratio diagram and Z DIST statistic, three distributions; GNO, GPA and GEV have been identified as the suitable candidates for regional distribution. Accuracy measures for the estimated regional growth curves and quantiles have been calculated for the three candidate distributions, using Monte Carlo simulations. Simulations study showed that GNO distribution is the robust distribution with GPA as suitable alternative but GEV is not an appropriate distribution for the study area.
tRNAile was isolated from E. coli Cp 79 (leu-, arg-, thr-, his-, thiamin-, RCrel) which had been grown on a sub-optimal concentration of thr and was found to contain an average of 50% less N-[9-(beta-D-ribofuranosyl)- purin-6-ylcarbamoyl]threonine, t6Ado, than tRNAile from cells grown on an optimum concentration of thr and containing a normal complement of t6Ado. The two tRNA's were identical in their ability to be aminoacylated, to accept the 3'-terminal dinucleotide, and to form an ile-tRNAile-Tu-GTP complex. In contrast, the t6Ado-deficient-tRNA was significantly less efficient in binding to ribosomes compared to the normal tRNA. This difference was seen in the binding of deacylated tRNA and in the nonenzymatic and enzymatic binding of ile-tRNA, all in response to poly AUC. The t6Ado-deficient ile-tRNA demonstrated no binding at Mg2+ concentrations less than or equal to 10 mM, while the normal ile-tRNA bound at low Mg2+ concentrations. Tetracycline had the same effect on the normal as on the t6Ado-deficient ile-tRNA binding. As a control, the binding of phe-tRNA (which does not contain t6Ado) from normal and thr-starved cells in response to poly U was identical. It was concluded that t6Ado is required for proper codon-anticodon interaction.
The paper presents results of an application of the L-moments based regional flood frequency analysis to annual maximum peak (AMP) flows observed at seven stations (Tarbela, Kalabagh, Chashma, Taunsa, Guddu, Sukkur and Kotri) located on the main stream of the Indus River, Pakistan. The results of Run-test and lag-1 correlation coefficient showed that the data series at given sites is random and has no serious serial correlations respectively. Furthermore, the results of Grubbs and Beck test illustrated that there are no irregularities (abrupt variations) except low outlier(s) in the data series at various sites. To avoid their undue influence, these low outliers have been discarded and the sample information has been resummarized using the idea of left censored type A partial probability weighted moments. L-moments based regional heterogeneity measure (H) showed that the region, defined by seven stations, is heterogeneous; therefore, it has been sub-divided into two homogeneous regions (Region 1 and Region 2 consist of four (Tarbela, Kalabagh, Chashma and Taunsa) and three sites (Guddu, Sukkur and Kotri, respectively) using Ward's clustering method based on the site characteristics only. The results of various goodness-of-fit measures (L-moment ratio diagram, average weighted distance and Z DIST measures) showed that Region 1 has four candidates: generalized normal (GNO), generalized logistic (GLO), generalized extreme-value (GEV) and Pearson type III (PE3), while Region 2 has only one candidate; GLO, as regional distribution. Based on the results of different accuracy measures (regional average absolute relative bias, relative bias and relative root mean square error) of the estimated regional growth curves and quantiles, obtained from simulation experiments, PE3 is the robust distribution for Region 1, while for Region 2, GLO distribution can be used for the estimation of flood quantiles. Moreover, the results of the simulations study have been extended to obtain standard errors of the estimated quantiles at each site of the sub-divided homogeneous regions.
The medical data are often filed for each patient in clinical studies in order to inform decision-making. Usually, medical data are generally skewed to the right, and skewed distributions can be the appropriate candidates in making inferences using Bayesian framework. Furthermore, the Bayesian estimators of skewed distribution can be used to tackle the problem of decision-making in medicine and health management under uncertainty. For medical diagnosis, physician can use the Bayesian estimators to quantify the effects of the evidence in increasing the probability that the patient has the particular disease considering the prior information. The present study focuses the development of Bayesian estimators for three-parameter Frechet distribution using noninformative prior and gamma prior under LINEX (linear exponential) and general entropy (GE) loss functions. Since the Bayesian estimators cannot be expressed in closed forms, approximate Bayesian estimates are discussed via Lindley鈥檚 approximation. These results are compared with their maximum likelihood counterpart using Monte Carlo simulations. Our results indicate that Bayesian estimators under general entropy loss function with noninformative prior (BGENP) provide the smallest mean square error for all sample sizes and different values of parameters. Furthermore, a data set about the survival times of a group of patients suffering from head and neck cancer is analyzed for illustration purposes.
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