A model of a snorer's upper airway

In snorers, the physiologic decrease of postural muscle tone during sleep results in increased collapsibility of the upper airway. Measurement of nasal pressure changes with prongs is increasingly used to monitor flow kinetics through a collapsing upper airway. This report presents a mathematical model to predict nasal flow profile from three critical components that control upper airway patency during sleep. The model includes the respiratory pump drive, the stiffness of the pharyngeal soft tissues, and the dynamic support of the muscles surrounding the upper airway. Depending on these three components, the proposed model is able to reproduce the characteristic changes in flow profile that are clinically observed in snorers and non-snorers during sleep.     

Medroxyprogesterone improves nocturnal breathing in postmenopausal women with chronic obstructive pulmonary disease

Background

Progestins as respiratory stimulants in chronic obstructive pulmonary disease (COPD) have been investigated in males and during wakefulness. However, sleep and gender may influence therapeutic responses. We investigated the effects of a 2-week medroxyprogesterone acetate (MPA) therapy on sleep and nocturnal breathing in postmenopausal women.

Methods

A single-blind placebo-controlled trial was performed in 15 postmenopausal women with moderate to severe COPD. A 12-week trial included 2-week treatment periods with placebo and MPA (60 mg/d/14 days). All patients underwent a polysomnography with monitoring of SaO₂ and transcutaneous PCO₂ (tcCO₂) at baseline, with placebo, with medroxyprogesterone acetate (MPA 60 mg/d/14 days), and three and six weeks after cessation of MPA.

Results

Thirteen patients completed the trial. At baseline, the average ± SD of SaO₂ mean was 90.6 ± 3.2 % and the median of SaO₂ nadir 84.8 % (interquartile range, IQR 6.1). MPA improved them by 1.7 ± 1.6 %-units (95 % confidence interval (CI) 0.56, 2.8) and by 3.9 %-units (IQR 4.9; 95% CI 0.24, 10.2), respectively. The average of tcCO₂ median was 6.0 ± 0.9 kPa and decreased with MPA by 0.9 ± 0.5 kPa (95% CI -1.3, -0.54). MPA improved SaO₂ nadir and tcCO₂ median also during REM sleep. Three weeks after cessation of MPA, the SaO₂ mean remained 1.4 ± 1.8 %-units higher than at baseline, the difference being not significant (95% CI -0.03, 2.8). SaO₂ nadir was 2.7 %-units (IQR 4.9; 95% CI 0.06, 18.7) higher than at baseline. Increases in SaO₂ mean and SaO₂ nadir during sleep with MPA were inversely associated with baseline SaO₂ mean (r = -0.70, p = 0.032) and baseline SaO₂ nadir (r = -0.77, p = 0.008), respectively. Treatment response in SaO₂ mean, SaO₂ nadir and tcCO₂ levels did not associate with pack-years smoked, age, BMI, spirometric results or sleep variables.

Conclusion

MPA-induced respiratory improvement in postmenopausal women seems to be consistent and prolonged. The improvement was greater in patients with lower baseline SaO₂ values. Long-term studies in females are warranted.     

Genome-Wide Scoring of Positive and Negative Epistasis through Decomposition of Quantitative Genetic Interaction Fitness Matrices

Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.     

Electrophoretic signal comparison applied to mRNA differential display analysis

Gene expression analysis by electrophoretic methods is currently limited by the labor-intensive visual evaluation of the electrophoretic signal profiles. For this purpose, we present a flexible approach to computer-assisted comparison of quantitative electrophoretic patterns between multiple expression signals. Gaussian curves are first fitted to the complex peak mixtures, and the resulting approximate signals are then aligned and compared on a peak-by-peak basis with respect to specific patterns defined by the investigator. The rationale of the method is to produce a compressed list of exceptional expression patterns quantified by a set of associated numeric features. A score value is attached to each pattern in such a way that large values identify the most potential findings to be focused on in visual analysis instead of the vast amount of original electrophoretic results. The validity of the method is demonstrated by analyzing a large set of electrophoretic data from mRNA differential display experiments monitoring changes in gene expression patterns in human colonic carcinoma. The automated identification of variously defined gene expression patterns agrees well with the visual evaluation of the same electropherograms. The general comparison approach may also be found useful with other gene expression profiling instruments.     

Efficient estimation of emission probabilities in profile hidden Markov models

MOTIVATION: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. RESULTS: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. AVAILABILITY: The algorithms written in C language are available on request from the authors.     

Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into the various sources of probe-level noise and gives tools to guide probe design.     

Reproducibility-optimized test statistic for ranking genes in microarray studies

A principal goal of microarray studies is to identify the genes showing differential expression under distinct conditions. In such studies, the selection of an optimal test statistic is a crucial challenge, which depends on the type and amount of data under analysis. While previous studies on simulated or spike-in datasets do not provide practical guidance on how to choose the best method for a given real dataset, we introduce an enhanced reproducibility-optimization procedure, which enables the selection of a suitable gene- anking statistic directly from the data. In comparison with existing ranking methods, the reproducibilityoptimized statistic shows good performance consistently under various simulated conditions and on Affymetrix spike-in dataset. Further, the feasibility of the novel statistic is confirmed in a practical research setting using data from an in-house cDNA microarray study of asthma-related gene expression changes. These results suggest that the procedure facilitates the selection of an appropriate test statistic for a given dataset without relying on a priori assumptions, which may bias the findings and their interpretation. Moreover, the general reproducibilityoptimization procedure is not limited to detecting differential expression only but could be extended to a wide range of other applications as well.     

Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies

The recent advent of exon microarrays has made it possible to reveal differences in alternative splicing events on a global scale. We introduce a novel statistical procedure that takes full advantage of the probe-level information on Affymetrix exon arrays when detecting differential splicing between sample groups. In comparison to existing ranking methods, the procedure shows superior reproducibility and accuracy in distinguishing true biological findings from background noise in high agreement with experimental validations.     

Model-Based Analysis of Mechanisms Responsible for Sleep-Induced Carbon Dioxide Differences

This work describes a comprehensive mathematical model of the human respiratory control system which incorporates the central mechanisms for predicting sleep-induced changes in chemical regulation of ventilation. The model integrates four individual compartments for gas storage and exchange, namely alveolar air, pulmonary blood, tissue capillary blood, body tissues, and gas transport between them. An essential mechanism in the carbon dioxide transport is its dissociation into bicarbonate and acid, where a buffering mechanism through hemoglobin is used to prevent harmfully low pH levels. In the current model, we assume high oxygen levels and consider intracellular hydrogen ion concentration as the principal respiratory control variable. The resulting system of delayed differential equations is solved numerically. With an appropriate choice of key parameters, such as velocity of blood flow and gain of a non-linear controller function, the model provides steady-state results consistent with our experimental observations measured in subjects across sleep onset. Dynamic predictions from the model give new insights into the behaviour of the system in subjects with different buffering capacities and suggest novel hypotheses for future experimental and clinical studies.