Driving Interactions Efficiently in a Composite Few-Body System

We study how to efficiently control an interacting few-body system consisting of three harmonically trapped bosons. Specifically, we investigate the process of modulating the inter-particle interactions to drive an initially non-interacting state to a strongly interacting one, which is an eigenstate of a chosen Hamiltonian. We also show that for unbalanced subsystems, where one can individually control the different inter- and intra-species interactions, complex dynamics originate when the symmetry of the ground state is broken by phase separation. However, as driving the dynamics too quickly can result in unwanted excitations of the final state, we optimize the driven processes using shortcuts to adiabaticity, which are designed to reduce these excitations at the end of the interaction ramp, ensuring that the target eigenstate is reached

Management of a pregnant woman with fibromuscular dysplasia

No abstract available

Single nucleotide polymorphisms in the apolipoprotein B and low density lipoprotein receptor genes affect response to antihypertensive treatment

BACKGROUND: Dyslipidemia has been associated with hypertension. The present study explored if polymorphisms in genes encoding proteins in lipid metabolism could be used as predictors for the individual response to antihypertensive treatment. METHODS: Ten single nucleotide polymorphisms (SNP) in genes related to lipid metabolism were analysed by a microarray based minisequencing system in DNA samples from ninety-seven hypertensive subjects randomised to treatment with either 150 mg of the angiotensin II type 1 receptor blocker irbesartan or 50 mg of the β(1)-adrenergic receptor blocker atenolol for twelve weeks. RESULTS: The reduction in blood pressure was similar in both treatment groups. The SNP C711T in the apolipoprotein B gene was associated with the blood pressure response to irbesartan with an average reduction of 19 mmHg in the individuals carrying the C-allele, but not to atenolol. The C16730T polymorphism in the low density lipoprotein receptor gene predicted the change in systolic blood pressure in the atenolol group with an average reduction of 14 mmHg in the individuals carrying the C-allele. CONCLUSIONS: Polymorphisms in genes encoding proteins in the lipid metabolism are associated with the response to antihypertensive treatment in a drug specific pattern. These results highlight the potential use of pharmacogenetics as a guide for individualised antihypertensive treatment, and also the role of lipids in blood pressure control

Categorisation of continuous covariates for stratified randomisation: How should we adjust?

To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate‐outcome relationship (via the link function) was either linear or non‐linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate‐outcome relationship or allowing for non‐linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate‐outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials

Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes

Many trials use stratified randomisation, where participants are randomised within strata defined by one or more baseline covariates. While it is important to adjust for stratification variables in the analysis, the appropriate method of adjustment is unclear when stratification variables are affected by misclassification and hence some participants are randomised in the incorrect stratum. We conducted a simulation study to compare methods of adjusting for stratification variables affected by misclassification in the analysis of continuous outcomes when all or only some stratification errors are discovered, and when the treatment effect or treatment-by-covariate interaction effect is of interest. The data were analysed using linear regression with no adjustment, adjustment for the strata used to perform the randomisation (randomisation strata), adjustment for the strata if all errors are corrected (true strata), and adjustment for the strata after some errors are discovered and corrected (updated strata). The unadjusted model performed poorly in all settings. Adjusting for the true strata was optimal, while the relative performance of adjusting for the randomisation strata or the updated strata varied depending on the setting. As the true strata are unlikely to be known with certainty in practice, we recommend using the updated strata for adjustment and performing subgroup analyses, provided the discovery of errors is unlikely to depend on treatment group, as expected in blinded trials. Greater transparency is needed in the reporting of stratification errors and how they were addressed in the analysis

Boston University Medical Center: Perspectives on Health Policy

Report of a symposium held at the Boston University Medical Center