University of Utah

About: University of Utah is a education organization based out in Salt Lake City, Utah, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52894 authors who have published 124076 publications receiving 5265834 citations. The organization is also known as: The U & The University of Utah.

Influence of the Genotype on the Clinical Course of the Long-QT Syndrome

Abstract: Background The congenital long-QT syndrome, caused by mutations in cardiac potassium-channel genes (KVLQT1 at the LQT1 locus and HERG at the LQT2 locus) and the sodium-channel gene (SCN5A at the LQT3 locus), has distinct repolarization patterns on electrocardiography, but it is not known whether the genotype influences the clinical course of the disease. Methods We determined the genotypes of 541 of 1378 members of 38 families enrolled in the International Long-QT Syndrome Registry: 112 had mutations at the LQT1 locus, 72 had mutations at the LQT2 locus, and 62 had mutations at the LQT3 locus. We determined the cumulative probability and lethality of cardiac events (syncope, aborted cardiac arrest, or sudden death) occurring from birth through the age of 40 years according to genotype in the 246 gene carriers and in all 1378 members of the families studied. Results The frequency of cardiac events was higher among subjects with mutations at the LQT1 locus (63 percent) or the LQT2 locus (46 percent) than am...

Toward a unified view of the American monsoon systems

Abstract: An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere–land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge.

Headwaters of the zebrafish — emergence of a new model vertebrate

Abstract: The understanding of vertebrate development has advanced considerably in recent years, primarily due to the study of a few model organisms. The zebrafish, the newest of these models, has risen to prominence because both genetic and experimental embryological methods can be easily applied to this animal. The combination of approaches has proven powerful, yielding insights into the formation and function of individual tissues, organ systems and neural networks, and into human disease mechanisms. Here, we provide a personal perspective on the history of zebrafish research, from the assembly of the first genetic and embryological tools through to sequencing of the genome.

Recombinant human tumor necrosis factor receptor (etanercept) for treating ankylosing spondylitis: a randomized, controlled trial.

Abstract: Objective To determine the safety and efficacy of etanercept in a multicenter, randomized, placebo-controlled, double-blind trial of adults with moderate to severe active ankylosing spondylitis (AS). Methods Patients (n = 277) were treated with either etanercept 25 mg (n = 138) or placebo (n = 139) subcutaneously twice weekly for 24 weeks. The primary outcome measures were the percentages of patients achieving the Assessments in Ankylosing Spondylitis 20% response (ASAS20) at weeks 12 and 24. Other outcome measures included the percentage of patients achieving higher ASAS responses, and the safety of etanercept in patients with AS. All outcome measures were assessed at 2, 4, 8, 12, and 24 weeks. Results Treatment with etanercept resulted in dramatic improvement. The ASAS20 was achieved by 59% of patients in the etanercept group and by 28% of patients in the placebo group (P < 0.0001) at week 12, and by 57% and 22% of patients, respectively, at week 24 (P < 0.0001). All individual ASAS components, acute-phase reactant levels, and spinal mobility measures were also significantly improved. The safety profile of etanercept was similar to that reported in studies of patients with rheumatoid arthritis or psoriatic arthritis. The only adverse events that occurred significantly more often in the etanercept group were injection-site reactions, accidental injuries, and upper respiratory tract infections. Conclusion Etanercept is a highly effective and well tolerated treatment in patients with active AS.

Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms.

Abstract: Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal- complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.