Control of successive unequal cell divisions by neural cell fate regulators determines embryonic neuroblast cell size.

Asymmetric cell divisions often generate daughter cells of unequal size in addition to different fates. In some contexts, daughter cell size asymmetry is thought to be a key input to specific binary cell fate decisions. An alternative possibility is that unequal division is a mechanism by which a variety of cells of different sizes are generated during embryonic development. We show here that two unequal cell divisions precede neuroblast formation in the C lineage of Caenorhabditis elegans. The equalisation of these divisions in a pig-1/MELK mutant background has little effect on neuroblast specification. Instead, we demonstrate that let-19/MDT13 is a regulator of the proneural basic helix-loop-helix transcription factor hlh-14/ASCL1 and find that both are required to concomitantly regulate the acquisition of neuroblast identity and neuroblast cell size. Thus, embryonic neuroblast cell size in this lineage is progressively regulated in parallel with identity by key neural cell fate regulators. We propose that key cell fate determinants have a previously unappreciated function in regulating unequal cleavage, and therefore cell size, of the progenitor cells whose daughter cell fates they then go on to specify.

Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes.

In this paper, we study non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring. First, the maximum likelihood estimates and maximum product spacings are derived. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Second, Bayesian estimators under symmetric and asymmetric loss functions (Squared error, linear exponential, and general entropy loss functions) are also obtained. The Lindley approximation and the Markov chain Monte Carlo method are used to derive the Bayesian estimates. Furthermore, we derive the highest posterior density credible intervals of the parameters. We further present an optimal progressive censoring scheme among different competing censoring scheme using three optimality criteria. Simulation studies are conducted to evaluate the performance of the point and interval estimators. Finally, one application of real data sets is provided to illustrate the proposed procedures.

Recent predictive parameters for successful weaning from mechanical ventilation in critically Ill patients

Background: Removal of patients from mechanical ventilation (MV) has been termed liberation, discontinuation, withdrawal and most commonly weaning. Weaning covers the entire process of liberating the patient from mechanical support and from the endotracheal tube. Although weaning from MV is successful in most cases, the first attempt fails in 20% of patients. In addition, weaning accounts for over 40% of the total MV time, the proportion varying in function of the etiology of respiratory failure. Objective: The aim of this study was to evaluate the recent protocols of successful weaning from mechanical ventilation of critically ill patients, depending on central venous oxygen saturation, ultrasonographic assessment of diaphragmatic movement, and serial arterial blood gases to assess failure rate 48 hours after weaning. Patients and methods: This prospective randomized study included a total of 90 mechanically ventilated Egyptian patients of both sexes, ASA (I-II) attending at least for 48 hours at intensive care unit, AlAzhar University Hospitals. The included subjects were divided into three groups depending on method of monitoring; group A: serial arterial blood gases, group B: Central venous oxygen saturation and group C: Ultrasonographic assessment of diaphragmatic movement pre and post spontaneous breathing trial. All patients were subjected to daily monitoring of the following weaning parameters: static and dynamic compliances and inspiratory resistance, intrinsic positive end expiratory pressure (Auto PEEP) and Maximum inspiratory pressure (MIP). Results: There is highly statistically significant difference between patients as regard weaning outcome. As the group depended on normal ultrasonographic assessment of diaphragmatic movement, had the largest number of patients with successful weaning. Conclusion: Normal ultrasonographic assessment of diaphragmatic movement proved to be the most important criteria for successful weaning from mechanical ventilation