A study of Erlang ETS table implementations and performance

The viability of implementing an in-memory database, Erlang ETS, using a relatively-new data structure, called a Judy array, was studied by comparing the performance of ETS tables based on four data structures: AVL balanced binary trees, B-trees, resizable linear hash tables, and Judy arrays. The benchmarks used workloads of sequentially- and randomly-ordered keys at table populations from 700 keys to 54 million keys

Pooling Community Data for Community Interventions When the Number of Pairs is Small

There is considerable interest in community interventions for health promotion, where the community is the experimental unit. Because such interventions are expensive, the number of experimental units (communities) is usually very small, yielding a study with low power. We examined the ability of a process known as “pooling” or “preliminary significance testing” to improve the power of community variations. In this process, one first tests whether there is significant community variation, using type 1 error of perhaps 0.25. If there is significant variation, the usual community-level test is performed. If not, a person-level test is performed. We found through Monte Carlo simulation that for studies with 2, 3, or 4 communities per group, this procedure could improve power somewhat in situations where the community by time variation is known to be small. Estimates of community by time variation for a variety of health variables are also presented. Because of the limited information available on community variances, and the probable difficulties in defending a person-level analysis, we recommend against the pooling procedure at this time

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http://archive.org/details/militarychaplain00lystNAN

COPD in young patients: A pre-specified analysis of the four-year trial of tiotropium (UPLIFT)

SummaryWhilst recent large-scale studies have provided much evidence on the natural history and therapeutic response in patients with chronic obstructive pulmonary disease (COPD), relatively little is known about the effect in younger patients.We report a pre-specified post-hoc analysis of 356 patients with COPD ≤ 50 years old from the four year randomised, double blind placebo controlled Understanding Potential Long Term Impact on Function with Tiotropium (UPLIFT) trial. Inclusion criteria included a post-bronchodilator forced expiratory volume in 1 s (FEV1) of ≤70%, FEV1/FVC < 0.70, age ≥40 years, and smoking history of ≥10 pack years.Younger patients had a mean FEV1 of 1.24 L (39% predicted) and an impaired health-related quality of life (St. George’s Respiratory Questionnaire (SGRQ)) compared to the entire UPLIFT population. There were 40.2% women and 51.1% current smokers in the younger age group. Tiotropium was associated with a sustained improvement in spirometry and SGRQ. Mean decline in post-bronchodilator FEV1 was 58 ml/year (placebo) vs. 38 ml/year (tiotropium) (p = 0.01). Corresponding values for pre-bronchodilator FEV1 were 41 ml/year (placebo) compared with 34 ml/year (tiotropium) (p = 0.34). The hazard ratio (95%CI) for an exacerbation in the younger age group was 0.87(0.68, 1.13)). The rate of exacerbations was reduced by tiotropium (rate ratio (95%CI) = 0.73(0.56, 0.95)).Tiotropium resulted in sustained bronchodilation, improved quality of life, and a decreased exacerbation rate in younger patients. Tiotropium also resulted in a significant reduction in the decline in post-bronchodilator FEV1, suggesting possible disease modification by tiotropium in younger patients with COPD

Fitting and Interpreting Continuous-Time Latent Markov Models for Panel Data

Multistate models are used to characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses according a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization (EM) algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a data-set of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting

Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains

Abstract Background Correctly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are used to identify each one. Datasets that span these two categories are often analyzed with a single program for peak calling combined with an ad hoc method for domains. Results We developed hiddenDomains, which identifies both peaks and domains, and compare it to the leading algorithms using H3K27me3, H3K36me3, GABP, ESR1 and FOXA ChIP-seq datasets. The output from the programs was compared to qPCR-validated enriched and depleted sites, predicted transcription factor binding sites, and highly-transcribed gene bodies. With every method, hiddenDomains, performed as well as, if not better than algorithms dedicated to a specific type of analysis. Conclusions hiddenDomains performs as well as the best domain and peak calling algorithms, making it ideal for analyzing ChIP-seq datasets, especially those that contain a mixture of peaks and domains

Genome-wide Linkage Scan to Identify Loci for Age at First Cigarette in Dutch Sibling Pairs

Most smokers begin smoking during adolescence and early age of smoking initiation is related to more frequent current smoking, daily smoking and more dependent smoking (Everret et al., 1999; Lando et al.

The Cyclic News Filesystem: Getting INN To Do More With Less

When Usenet News servers were first implemented, the design principle of storing each Usenet article in a separate file appeared to be sound. However, the number of Usenet News articles posted per day has grown phenomenally in the past decade and shows no sign of abating. To stay ahead of the growth curve, Usenet administrators have been forced to buy faster machines, more RAM, and many more disk drives. Many of the performance limitations are caused by interactions with the underlying OS&apos;s filesystem, which is usually a Berkeley Fast Filesystem (FFS) derivative. The Cyclic News Filesystem (CNFS) was designed to avoid most of FFS&apos;s major problems when used with INN: synchronous file linking/unlinking and sequential scanning of directory files. Articles are stored within a relative handful of large files, either as regular files on top of a standard filesystem or as block disk devices. Articles are stored sequentially within each file, resuming at the beginning of the file when the end ..