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William Casey

Publications and source records attributed to William Casey.

6 recordsLinked to original sources
PubMed2004

The McCoy straight blade does not improve laryngoscopy and intubation in normal infants.

PURPOSE: The McCoy curved blade laryngoscope has been demonstrated to improve view at laryngoscopy in adults. A straight-bladed version of this laryngoscope has recently been introduced into pediatric practice. The objective of this prospective, randomized study was to compare the intubating conditions afforded by the McCoy (#1) straight blade laryngoscope with the conventional Miller (#1) blade in neonates and infants. METHODS: Forty patients under six months of age, were randomized into two groups: one group (n = 20) had an initial laryngoscopy with the McCoy blade and then had a laryngoscopy and subsequent intubation using the Miller blade; the second group (n = 20) had an initial laryngoscopy with the Miller blade, followed by laryngoscopy and intubation using the McCoy blade. All intubations were performed by one anesthesiologist familiar with using both blades. RESULTS: The majority of patients (39 out of 40) had a similar or superior view (Cormack and Lehane classification) with the Miller when compared with the McCoy laryngoscope. Mean time to laryngoscopy was 14.9 (12.7) sec with the McCoy and 6.8 (2.07) sec with the Miller blade (P = 0.001), whereas mean time to intubation was 25.13 (10.4) sec with the McCoy and 12 (8.5) sec with the Miller blade (P = 0.014). There was no difference between the groups regarding desaturation and changes in heart rate during laryngoscopy and intubation. CONCLUSION: Our data indicate that the McCoy blade has no advantage over the conventional pediatric Miller blade in normal infants.

Analysis of Variance↗
PubMed2003

Minimal entropy probability paths between genome families.

We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non-rich vectors, does not involve variational theory and does not involve differential equations, but is a better approximation of the minimal entropy path distance than the distance //b-a//(2). We compute minimal entropy distance matrices for examples of DNA myostatin genes and amino-acid sequences across several species. Output tree dendograms for our minimal entropy metric are compared with dendograms based on BLAST and BLAST identity scores.

Algorithms↗
PubMed2003

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.

Animals↗
PubMedDate not supplied

Validation of S. pombe sequence assembly by microarray hybridization.

We describe a method to make physical maps of genomes using correlative hybridization patterns of probes to random pools of BACs. We derive thereby an estimated distance between probes, and then use this estimated distance to order probes. To test the method, we used BAC libraries from Schizzosaccharomyces pombe. We compared our data to the known sequence assembly, in order to assess accuracy. We demonstrate a small number of significant discrepancies between our method and the map derived by sequence assembly. Some of these discrepancies may arise because genome order within a population is not stable; imposing a linear order on a population may not be biologically meaningful.

Algorithms↗