896 research outputs found
Spontaneous cytokine production in children according to biological characteristics and environmental exposures.
BACKGROUND: Environmental factors are likely to have profound effects on the development of host immune responses, with serious implications for infectious diseases and inflammatory disorders such as asthma. OBJECTIVE: This study was designed to investigate the effects of environmental exposures on the cytokine profile of children. METHODS: The study involved measurement of T helper (Th) 1 (interferon-gamma), 2 [interleukin (IL)-5 and IL-13], and the regulatory cytokine IL-10 in unstimulated peripheral blood leukocytes from 1,376 children 4-11 years of age living in a poor urban area of the tropics. We also assessed the impact of environmental exposures in addition to biological characteristics recorded at the time of blood collection and earlier in childhood (0-3 years before blood collection). RESULTS: The proportion of children producing IL-10 was greater among those without access to drinking water [p < 0.05, chi-square test, odds ratio (OR) = 1.67]. The proportion of children producing IL-5 and IL-10 (OR = 10.76) was significantly greater in households that had never had a sewage system (p < 0.05, trend test). CONCLUSIONS: These data provide evidence for the profound effects of environmental exposures in early life as well as immune homeostasis in later childhood. Decreased hygiene (lack of access to clean drinking water and sanitation) in the first 3 years of life is associated with higher spontaneous IL-10 production up to 8 years later in life
Data production models for the CDF experiment
The data production for the CDF experiment is conducted on a large Linux PC
farm designed to meet the needs of data collection at a maximum rate of 40
MByte/sec. We present two data production models that exploits advances in
computing and communication technology. The first production farm is a
centralized system that has achieved a stable data processing rate of
approximately 2 TByte per day. The recently upgraded farm is migrated to the
SAM (Sequential Access to data via Metadata) data handling system. The software
and hardware of the CDF production farms has been successful in providing large
computing and data throughput capacity to the experiment.Comment: 8 pages, 9 figures; presented at HPC Asia2005, Beijing, China, Nov 30
- Dec 3, 200
Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data
Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects.
Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding.
Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping.
Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression.
Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets
Data processing model for the CDF experiment
The data processing model for the CDF experiment is described. Data
processing reconstructs events from parallel data streams taken with different
combinations of physics event triggers and further splits the events into
datasets of specialized physics datasets. The design of the processing control
system faces strict requirements on bookkeeping records, which trace the status
of data files and event contents during processing and storage. The computing
architecture was updated to meet the mass data flow of the Run II data
collection, recently upgraded to a maximum rate of 40 MByte/sec. The data
processing facility consists of a large cluster of Linux computers with data
movement managed by the CDF data handling system to a multi-petaByte Enstore
tape library. The latest processing cycle has achieved a stable speed of 35
MByte/sec (3 TByte/day). It can be readily scaled by increasing CPU and
data-handling capacity as required.Comment: 12 pages, 10 figures, submitted to IEEE-TN
Serum Perfluorooctanoate (PFOA) and Perfluorooctane Sulfonate (PFOS) Concentrations and Liver Function Biomarkers in a Population with Elevated PFOA Exposure
Background: Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) persist in the environment and are found in relatively high concentrations in animal livers. Studies in humans have reported inconsistent associations between PFOA and liver enzymes
Heart Failure with Preserved and Reduced Ejection Fraction in Hemodialysis Patients: Prevalence, Disease Prediction and Prognosis
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers.
BACKGROUND: Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists' hypotheses about the underlying biological mechanisms to be integrated. RESULTS: We present an analytical approach for statistical analysis of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. CONCLUSION: The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes
Measurement of the Boson Mass
A measurement of the mass of the boson is presented based on a sample of
5982 decays observed in collisions at
= 1.8~TeV with the D\O\ detector during the 1992--1993 run. From a
fit to the transverse mass spectrum, combined with measurements of the
boson mass, the boson mass is measured to be .Comment: 12 pages, LaTex, style Revtex, including 3 postscript figures
(submitted to PRL
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