Relationship of CSF pH, O2, and CO2 responses in metabolic acidosis and alkalosis in humans

The effect of induced metabolic acidosis (48 h of NH4Cl ingestion, BE - 10.6 +/- 1.1) and alkalosis (43 h of NaHCO3- ingestion BE 8.8 +/- 1.6) on arterial and lumber CSF pH, Pco2, and HCO3- and ventilatory responses to CO2 and to hypoxia was assessed in five healthy men. In acidosis lumbar CSF pH rose 0.033 +/- 0.02 (P less than 0.05). In alkalosis CSF pH was unchanged. Ventilatory response lines to CO2 at high O2 were displaced to the left in acidosis (9.0 +/- 1.4 Torr) and to the right in alkalosis (4.5 +/- 1.5 Torr) with no change in slope. The ventilatory response to hypoxia (delta V40) was increased in acidosis (P less than 0.05) and it was decreased in four subjects in alkalosis (P, not significant). We conclude that the altered ventilatory drives of steady-state metabolic imbalance are mediated by peripheral chemoreceptors, and in acidosis the medullary respiratory chemoreceptor drive is decreased.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.