Folding and insertion thermodynamics of the transmembrane WALP peptide

The anchor of most integral membrane proteins consists of one or several helices spanning the lipid bilayer. The WALP peptide, GWW(LA)$_n$(L)WWA, is a common model helix to study the fundamentals of protein insertion and folding, as well as helix-helix association in the membrane. Its structural properties have been illuminated in a large number of experimental and simulation studies. In this combined coarse-grained and atomistic simulation study, we probe the thermodynamics of a single WALP peptide, focusing on both the insertion across the water-membrane interface, as well as folding in both water and a membrane. The potential of mean force characterizing the peptide's insertion into the membrane shows qualitatively similar behavior across peptides and three force fields. However, the Martini force field exhibits a pronounced secondary minimum for an adsorbed interfacial state, which may even become the global minimum---in contrast to both atomistic simulations and the alternative PLUM force field. Even though the two coarse-grained models reproduce the free energy of insertion of individual amino acids side chains, they both underestimate its corresponding value for the full peptide (as compared with atomistic simulations), hinting at cooperative physics beyond the residue level. Folding of WALP in the two environments indicates the helix as the most stable structure, though with different relative stabilities and chain-length dependence.Comment: 12 pages, 5 figure

Bootstrap Methods: A Guide for Practitioners and Researchers

Includes bibliographical references (p. 188-329) and indexes.1. What is bootstrapping? -- 1.1. Background -- 1.2. Introduction -- 1.3. Wide range of applications -- 1.4. Historical notes -- 1.5. Summary -- 2. Estimation -- 2.1. Estimating bias -- 2.1.1. How to do it by bootstrapping -- 2.1.2. Error rate estimation in discrimination -- 2.1.3. Error rate estimation: an illustrative problem -- 2.1.4. Efron's patch data example -- 2.2. Estimating location and dispersion -- 2.2.1. Means and medians -- 2.2.2. Standard errors and quartiles -- 2.3. Historical notes -- 3. Confidence sets and hypothesis testing -- 3.1. Confidence sets -- 3.1.1. Typical value theorems for M-estimates -- 3.1.2. Percentile method -- 3.1.3. Bias Correction and the Acceleration Constant -- 3.1.4. Iterated Bootstrap -- 3.1.5. Bootstrap Percentile t Confidence Intervals -- 3.2. Relationship Between Confidence Intervals and Tests of Hypotheses -- 3.3. Hypothesis Testing Problems -- 3.3.1. Tendril DX Lead Clinical Trial Analysis --^3.4. Application of bootstrap confidence Intervals to Binary Dose-Response Modeling -- 3.5. Historical Notes -- 4. Regression analysis -- 4.1. Linear Models -- 4.1.1. Gauss-Markov Theory -- 4.1.2. Why Not Just Use Least Squares? -- 4.1.3. Should I Bootstrap the Residuals from the Fit? -- 4.2. Nonlinear Models -- 4.2.1. Examples of Nonlinear Models -- 4.2.2. Quasi-optical Experiment -- 4.3. Nonparametric Models -- 4.4. Historical Notes -- 5. Forecasting and Time Series Analysis -- 5.1. Methods of Forecasting -- 5.2. Time Series Models -- 5.3. When Does Bootstrapping Help with Prediction Intervals? -- 5.4. Model-Based Versus Block Resampling -- 5.5. Explosive Autoregressive Processes -- 5.6. Bootstrapping-Stationary Arma Models -- 5.7. Frequency-Based Approaches -- 5.8. Sieve Bootstrap -- 5.9. Historical notes -- 6. Which resampling Method Should You Use? -- 6.1. Related Methods -- 6.1.1. Jackknife -- 6.1.2. Delta Method, Infinitesimal Jackknife, and Influence Functions --^6.1.3. Cross-Validation -- 6.1.4. Subsampling -- 6.2. Bootstrap variants -- 6.2.1. Bayesian bootstrap -- 6.2.2. Smoothed bootstrap -- 6.2.3. Parametric Bootstrap -- 6.2.4. Double bootstrap -- 6.2.5. m-out-of-n Bootstrap -- 7. Efficient and Effective Simulation -- 7.1. How Many Replications? -- 7.2. Variance Reduction Methods -- 7.2.1. Linear Approximation -- 7.2.2. Balanced Resampling -- 7.2.3. Antithetic Variates -- 7.2.4. Importance Sampling -- 7.2.5. Centering -- 7.3. When Can Monte Carlo Be Avoided? -- 7.4. Historical Notes -- 8. Special Topics -- 8.1. Spatial Data -- 8.1.1. Kriging -- 8.1.2. Block Bootstrap on Regular Grids -- 8.1.3. Block Bootstrap on Irregular Grids -- 8.2. Subset Selection -- 8.3. Determining the Number of Distributions in a Mixture Model -- 8.4. Censored Data -- 8.5. p-Value Adjustment -- 8.5.1. Description of Westfall-Young Approach -- 8.5.2. Passive Plus OX Example -- 8.5.3. Consulting Example -- 8.6. Bioequivalence Applications --^8.6.1. Individual Bioequivalence -- 8.6.2. Population Bioequivalence -- 8.7. Process Capability Indices -- 8.8. Missing Data -- 8.9. Point Processes -- 8.10. Lattice Variables -- 8.11. Historical Notes -- 9. When Bootstrapping Fails Along with Remedies for Failures -- 9.1. Too Small of a Sample Size -- 9.2. Distributions with Infinite Moments -- 9.2.1. Introduction -- 9.2.2. Example of Inconsistency -- 9.2.3. Remedies -- 9.3. Estimating Extreme Values -- 9.3.1. Introduction -- 9.3.2. Example of Inconsistency -- 9.3.3. Remedies -- 9.4. Survey Sampling -- 9.4.1. Introduction -- 9.4.2. Example of Inconsistency -- 9.4.3. Remedies -- 9.5. Data Sequences that Are M-Dependent -- 9.5.1. Introduction -- 9.5.2. Example of Inconsistency When Independence Is Assumed -- 9.5.3. Remedies -- 9.6. Unstable Autoregressive Processes -- 9.6.1. Introduction -- 9.6.2. Example of Inconsistency -- 9.6.3. Remedies -- 9.7. Long-Range Dependence -- 9.7.1. Introduction -- 9.7.2. Example of Inconsistency --^9.7.3. Remedies -- 9.8. Bootstrap Diagnostics -- 9.9. Historical Notes

Strongly exchange-coupled triplet pairs in an organic semiconductor

From biological complexes to devices based on organic semiconductors, spin interactions play a key role in the function of molecular systems. For instance, triplet-pair reactions impact operation of organic light-emitting diodes as well as photovoltaic devices. Conventional models for triplet pairs assume they interact only weakly. Here, using electron spin resonance, we observe long-lived, strongly-interacting triplet pairs in an organic semiconductor, generated via singlet fission. Using coherent spin-manipulation of these two-triplet states, we identify exchange-coupled (spin-2) quintet complexes co-existing with weakly coupled (spin-1) triplets. We measure strongly coupled pairs with a lifetime approaching 3 µs and a spin coherence time approaching 1 µs, at 10 K. Our results pave the way for the utilization of high-spin systems in organic semiconductors.Gates-Cambridge Trust, Winton Programme for the Physics of Sustainability, Freie Universität Berlin within the Excellence Initiative of the German Research Foundation, Engineering and Physical Sciences Research Council (Grant ID: EP/G060738/1)This is the author accepted manuscript. The final version is available from Nature Publishing Group at http://dx.doi.org/10.1038/nphys3908

Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial

IMPORTANCE: Secretory phospholipase A2(sPLA2) generates bioactive phospholipid products implicated in atherosclerosis. The sPLA2inhibitor varespladib has favorable effects on lipid and inflammatory markers; however, its effect on cardiovascular outcomes is unknown. OBJECTIVE: To determine the effects of sPLA2inhibition with varespladib on cardiovascular outcomes. DESIGN, SETTING, AND PARTICIPANTS: A double-blind, randomized, multicenter trial at 362 academic and community hospitals in Europe, Australia, New Zealand, India, and North America of 5145 patients randomized within 96 hours of presentation of an acute coronary syndrome (ACS) to either varespladib (n = 2572) or placebo (n = 2573) with enrollment between June 1, 2010, and March 7, 2012 (study termination on March 9, 2012). INTERVENTIONS: Participants were randomized to receive varespladib (500 mg) or placebo daily for 16 weeks, in addition to atorvastatin and other established therapies. MAIN OUTCOMES AND MEASURES: The primary efficacy measurewas a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), nonfatal stroke, or unstable angina with evidence of ischemia requiring hospitalization at 16 weeks. Six-month survival status was also evaluated. RESULTS: At a prespecified interim analysis, including 212 primary end point events, the independent data and safety monitoring board recommended termination of the trial for futility and possible harm. The primary end point occurred in 136 patients (6.1%) treated with varespladib compared with 109 patients (5.1%) treated with placebo (hazard ratio [HR], 1.25; 95%CI, 0.97-1.61; log-rank P = .08). Varespladib was associated with a greater risk of MI (78 [3.4%] vs 47 [2.2%]; HR, 1.66; 95%CI, 1.16-2.39; log-rank P = .005). The composite secondary end point of cardiovascular mortality, MI, and stroke was observed in 107 patients (4.6%) in the varespladib group and 79 patients (3.8%) in the placebo group (HR, 1.36; 95% CI, 1.02-1.82; P = .04). CONCLUSIONS AND RELEVANCE: In patients with recent ACS, varespladib did not reduce the risk of recurrent cardiovascular events and significantly increased the risk of MI. The sPLA2inhibition with varespladib may be harmful and is not a useful strategy to reduce adverse cardiovascular outcomes after ACS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01130246. Copyright 2014 American Medical Association. All rights reserved

The RESOLVE Survey Atomic Gas Census and Environmental Influences on Galaxy Gas Reservoirs

We present the H i mass inventory for the REsolved Spectroscopy Of a Local VolumE (RESOLVE) survey, a volume-limited, multi-wavelength census of >1500 z = 0 galaxies spanning diverse environments and complete in baryonic mass down to dwarfs of ~109 ${M}_{\odot }$. This first 21 cm data release provides robust detections or strong upper limits (1.4M H i 1012 ${M}_{\odot }$) halos, suggesting that gas stripping and/or starvation may be induced by interactions with larger halos or the surrounding cosmic web. We find that the detailed relationship between G/S and environment varies when we examine different subvolumes of RESOLVE independently, which we suggest may be a signature of assembly bias

Cost-effectiveness of minimal interventional procedures for chronic mechanical low back pain: design of four randomised controlled trials with an economic evaluation

Background: Minimal interventional procedures are frequently applied in patients with mechanical low back pain which is defined as pain presumably resulting from single sources: facet, disc, sacroiliac joint or a combination of these. Usually, these minimal interventional procedures are an integral part of a multidisciplinary pain programme. A recent systematic review issued by the Dutch Health Insurance Council showed that the effectiveness of these procedures for the total group of patients with chronic low back pain is yet unclear and cost-effectiveness unknown. The aim of the study is to evaluate whether a multidisciplinary pain programme with minimal interventional procedures is cost-effective compared to the multidisciplinary pain programme alone for patients with chronic mechanical low back pain who did not respond to conservative primary care and were referred to a pain clinic. Methods. All patients with chronic low back pain who are referred to one of the 13 participating pain clinics will be asked to participate in an observational study. Patients with a suspected diagnosis of facet, disc or sacroiliac joint problems will receive a diagnostic block to confirm this diagnosis. If confirmed, they will be asked to participate in a Randomized Controlled Trial (RCT). For each single source a separate RCT will be conducted. Patients with a combination of facet, disc or sacroiliac joint problems will be invited for participation in a RCT as well. An economic evaluation from a societal perspective will be performed alongside these four RCTs. Patients will complete questionnaires at baseline, 3 and 6 weeks, 3, 6, 9 and 12 months after start of the treatment

Application of chemometric analysis to infrared spectroscopy for the identification of wood origin

Chemical characteristics of wood are used in this study for plant taxonomy classification based on the current Angiosperm Phylogeny Group classification (APG III System) for the division, class and subclass of woody plants. Infrared spectra contain information about the molecular structure and intermolecular interactions among the components in wood but the understanding of this information requires multivariate techniques for the analysis of highly dense datasets. This article is written with the purposes of specifying the chemical differences among taxonomic groups, and predicting the taxa of unknown samples with a mathematical model. Principal component analysis, t-test, stepwise discriminant analysis and linear discriminant analysis, were some of the chosen multivariate techniques. A procedure to determine the division, class, subclass and order of unknown samples was built with promising implications for future applications of Fourier Transform Infrared spectroscopy in wood taxonomy classification

Effect of medication review and cognitive behaviour treatment by community pharmacists of patients discharged from the hospital on drug related problems and compliance: design of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Drug related problems (DRPs) are common among elderly patients who are discharged from the hospital and are using several drugs for their chronic diseases. Examples of drug related problems are contra-indications, interactions, adverse drug reactions and inefficacy of treatment. Causes of these problems include prescription errors and non-compliance with treatment. The aim of this study is to examine the effect of <it>medication review </it>and <it>cognitive behaviour therapy </it>of discharged patients by community pharmacists to minimize the occurrence of drug related problems.</p> <p>Methods/Design</p> <p>A randomized controlled trial will be performed. Community pharmacists will be randomized into a control group and an intervention group. 342 Patients, aged over 60 years, discharged from general and academic hospitals, using five or more prescription drugs for their chronic disease will be asked by their pharmacy to participate in the study.</p> <p>Patients randomized to the control group will receive usual care according to the Dutch Pharmacy Standard. The medication of patients randomised to the intervention group will be reviewed by the community pharmacist with use of the national guidelines for the treatment of diseases, when patients are discharged from the hospital. The Pharmaceutical Care network Europe Registration form will be used to record drug related problems. Trained pharmacy technicians will counsel patients at home at baseline and at 1,3,6,9 and 12 months, using Cognitive Behaviour Treatment according to the Theory of Planned Behaviour. The patient's attitude towards medication and patient's adherence will be subject of the cognitive behaviour treatment. The counselling methods that will be used are <it>motivational interviewing </it>and <it>problem solving treatment</it>. Patients adherence towards drug use will be determined with use of the Medication Adherence Report Scale Questionnaire. There will be a follow-up of 12 months.</p> <p>The two primary outcome measures are the difference in occurrence of DRPs between intervention and control group and adherence with drug use. Secondary endpoints are attitude towards drug use, incidence of Re-hospitalisations related to medicines, functional status of the patient, quality of life and the cost-effectiveness of this intervention.</p> <p>Discussion</p> <p>Combining both medication review and Cognitive Behaviour Treatment may decrease DRPs and may result in more compliance with drug use among patients discharged from the hospital and using 5 or more chronic drugs.</p> <p>Trial registration</p> <p>Dutch Trial Register NTR1194</p

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context