Lipid profiles for antiretroviral-naive patients starting PI- and NNRTI-based therapy in the Swiss HIV cohort study

BACKGROUND: Blood lipid abnormalities in patients on highly active antiretroviral therapy (HAART) have been associated with exposure to protease inhibitors (PIs), particularly ritonavir. First therapy with a non-nucleoside reverse transcriptase inhibitor (NNRTI) leads to relatively favourable lipid profiles. We report on medium-term lipid profiles (up to 5 years) for antiretroviral-naive patients starting NNRTI- and PI-based HAART in the Swiss HIV Cohort Study. METHODS: Since April 2000, blood samples taken at visits scheduled every 6 months have been analysed for cholesterol and triglyceride concentrations. For 1065 antiretroviral-naive patients starting HAART after April 2000, we estimated changes in concentration over time using multivariate linear regression with adjustment for baseline covariates, use of lipid-lowering drugs and whether the sample was taken in a fasting state. RESULTS: Non-high density lipoprotein (HDL) cholesterol levels increase with increasing exposure to either PI- or NNRTI-based therapy, HDL cholesterol levels increase and triglyceride levels decrease with increasing exposure to NNRTI-based therapy, whereas triglyceride levels increase with increasing exposure to PI-based therapy. Between NNRTI-based therapies, there is a slight difference in triglyceride levels, which tend to increase with increasing exposure to efavirenz and to decrease with increasing exposure to nevirapine. Of the three common PI-based therapies, nelfinavir appears to have a relatively favourable lipid profile, with little change with increasing exposure. Of the other two PI therapies, lopinavir with ritonavir has a more favourable profile than indinavir with ritonavir, with smaller increases in both non-HDL cholesterol and triglycerides and an increase in HDL cholesterol. Increasing exposure to abacavir is associated with a decrease in the level of triglycerides. CONCLUSION: In general, NNRTI-based therapy is associated with a more favourable lipid profile than PI-based therapy, but different PI-based therapies are associated with very different lipid profiles. Nelfinavir appears to have a relatively favourable lipid profile. Of the two boosted PI therapies, lopinavir appears to have a more favourable lipid profile than indinavir.

Retention of canopy trees as biological legacies for balancing woody biomass production and biodiversity in managed aspen forests of the Great Lakes Region

Green-tree retention under the conceptual framework of ecological forestry has the potential to provide both biomass feedstock for industry and maintain quality wildlife habitat. I examined the effects of retained canopy trees as biological legacies (“legacy trees”) in aspen (Populus spp.) forests on above-ground live woody biomass, understory plant floristic quality, and bird diversity. Additionally, I evaluated habitat quality for a high conservation priority species, the Golden-winged Warbler (Vermivora chrysoptera). I selected 27 aspen-dominated forest stands in northern Wisconsin with nine stands in each of three legacy tree retention treatments (conifer retention, hardwood retention, and clearcuts or no retention) across a chronosequence (4-36 years post-harvest). Conifer retention stands had greater legacy tree and all tree species biomass but lower regenerating tree biomass than clearcuts. Coniferous but not hardwood legacy trees appeared to suppress regenerating tree biomass. I evaluated the floristic quality of the understory plant assemblage by estimating the mean coefficient of conservatism (C). Mean C was lower in young stands than in middle-age or old stands; there was a marginally significant (p=0.058) interaction effect between legacy tree retention treatment and stand age. Late-seral plant species were positively associated with stand age and legacy tree diameter or age revealing an important relationship between legacy tree retention and stand development. Bird species richness was greatest in stands with hardwood retention particularly early in stand development. Six conservation priority bird species were indicators of legacy tree retention or clearcuts. Retention of legacy trees in aspen stands provided higher quality nest habitat for the Golden-winged Warbler than clearcuts based on high pairing success and nesting activity. Retention of hardwoods, particularly northern red oak (Quercus rubra), yielded the most consistent positive effects in this study with the highest bird species richness and the highest quality habitat for the Golden-winged Warbler. This treatment maintained stand biomass comparable to clearcuts and did not suppress regenerating tree biomass. In conclusion, legacy tree retention can enhance even-aged management techniques to produce a win-win scenario for the conservation of declining bird species and late-seral understory plants and for production of woody biomass feedstock from naturally regenerating aspen forests.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

Descriptive Statistics and Linear Regression

This morning Dr. Battle will introduce descriptive statistics and linear regression and how to apply these concepts in mathematical modeling. You will also learn how to use a spreadsheet to help with statistical analysis and to create graphs.

Strengthening a Traditional Fire Protection Course by Using Fault Trees

Traditional courses and textbooks in occupational safety emphasize rules, standards, and guidelines. This paper describes the early stage of a project to upgrade a traditional college course on fire protection by incorporating learning materials to develop the higher-level cognitive ability known as synthesis. Students will be challenged to synthesize textbook information into fault tree diagrams. The paper explains the place of synthesis in Bloom’s taxonomy of cognitive abilities and the utility of fault trees diagrams as a tool for synthesis. The intended benefits for students are: improved abilities to synthesize, a deeper understanding of fire protection practices, ability to construct fault trees for a wide range of undesired occurrences, and perhaps recognition that heavy reliance on memorization is the hard way to learn occupational safety and health.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

Are there risk factors in alpine skiing? A controlled multicentre survey of 1278 skiers

OBJECTIVE: To analyse risk factors in alpine skiing. DESIGN: A controlled multicentre survey of injured and non-injured alpine skiers. SETTING: One tertiary and two secondary trauma centres in Bern, Switzerland. PATIENTS AND METHODS: All injured skiers admitted from November 2007 to April 2008 were analysed using a completed questionnaire incorporating 15 parameters. The same questionnaire was distributed to non-injured controls. Multiple logistic regression was performed. Patterns of combined risk factors were calculated by inference trees. A total of 782 patients and 496 controls were interviewed. RESULTS: Parameters that were significant for the patients were: high readiness for risk (p = 0.0365, OR 1.84, 95% CI 1.04 to 3.27); low readiness for speed (p = 0.0008, OR 0.29, 95% CI 0.14 to 0.60); no aggressive behaviour on slopes (p<0.0001, OR 0.19, 95% CI 0.09 to 0.37); new skiing equipment (p = 0.0228, OR 59, 95% CI 0.37 to 0.93); warm-up performed (p = 0.0015, OR 1.79, 95% CI 1.25 to 2.57); old snow compared with fresh snow (p = 0.0155, OR 0.31, 95% CI 0.12 to 0.80); old snow compared with artificial snow (p = 0.0037, OR 0.21, 95% CI 0.07 to 0.60); powder snow compared with slushy snow (p = 0.0035, OR 0.25, 95% CI 0.10 to 0.63); drug consumption (p = 0.0044, OR 5.92, 95% CI 1.74 to 20.11); and alcohol abstinence (p<0.0001, OR 0.14, 95% CI 0.05 to 0.34). Three groups at risk were detected: (1) warm-up 3-12 min, visual analogue scale (VAS)(speed) >4 and bad weather/visibility; (2) VAS(speed) 4-7, icy slopes and not wearing a helmet; (3) warm-up >12 min and new skiing equipment. CONCLUSIONS: Low speed, high readiness for risk, new skiing equipment, old and powder snow, and drug consumption are significant risk factors when skiing. Future work should aim to identify more precisely specific groups at risk and develop recommendations--for example, a snow weather index at valley stations.

Ultrasonic acoustic emissions in drought-stressed trees - more than signals from cavitation?

Ultrasonic acoustic emission (UAE) in trees is often related to collapsing water columns in the flow path as a result of tensions that are too strong (cavitation). However, in a decibel (dB) range below that associated with cavitation, a close relationship was found between UAE intensities and stem radius changes. • UAE was continuously recorded on the stems of mature field-grown trees of Scots pine (Pinus sylvestris) and pubescent oak (Quercus pubescens) at a dry inner-Alpine site in Switzerland over two seasons. The averaged 20-Hz records were related to microclimatic conditions in air and soil, sap-flow rates and stem-radius fluctuations de-trended for growth (ΔW). • Within a low-dB range (27 ± 1 dB), UAE regularly increased and decreased in a diurnal rhythm in parallel with ΔW on cloudy days and at night. These low-dB emissions were interrupted by UAE abruptly switching between the low-dB range and a high-dB range (36 ± 1 dB) on clear, sunny days, corresponding to the widely supported interpretation of UAE as sound from cavitations. • It is hypothesized that the low-dB signals in drought-stressed trees are caused by respiration and/or cambial growth as these physiological activities are tissue water-content dependent and have been shown to produce courses of CO2 efflux similar to our courses of ΔW and low-dB UAE.

Virological and immunological responses to efavirenz or boosted lopinavir as first-line therapy for patients with HIV

BACKGROUND: Efavirenz and lopinavir boosted with ritonavir are both recommended as first-line therapies for patients with HIV when combined with two nucleoside reverse transcriptase inhibitors. It is uncertain which therapy is more effective for patients starting therapy with an advanced infection. METHODS: We estimated the relative effect of these two therapies on rates of virological and immunological failure within the Swiss HIV Cohort Study and considered whether estimates depended on the CD4(+) T-cell count when starting therapy. We defined virological failure as either an incomplete virological response or viral rebound after viral suppression and immunological failure as failure to achieve an expected CD4(+) T-cell increase calculated from EuroSIDA statistics. RESULTS: Patients starting efavirenz (n=660) and lopinavir (n=541) were followed for a median of 4.5 and 3.1 years, respectively. Virological failure was less likely for patients on efavirenz, with the adjusted hazard ratio (95% confidence interval) of 0.63 (0.50-0.78) then multiplied by a factor of 1.00 (0.90-1.12) for each 100 cells/mm(3) decrease in CD4(+) T-cell count below the mean when starting therapy. Immunological failure was also less likely for patients on efavirenz, with the adjusted hazard ratio of 0.68 (0.51-0.91) then multiplied by a factor of 1.29 (1.14-1.46) for each 100 cells/mm(3) decrease in CD4(+) T-cell count below the mean when starting therapy. CONCLUSIONS: Virological failure is less likely with efavirenz regardless of the CD4(+) T-cell count when starting therapy. Immunological failure is also less likely with efavirenz; however, this advantage disappears if patients start therapy with a low CD4(+) T-cell count.

A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.