Activating teaching methods, studying responses and learning

Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.Peer Reviewe

Strongly aligned gas-phase molecules at Free-Electron Lasers

We demonstrate a novel experimental implementation to strongly align molecules at full repetition rates of free-electron lasers. We utilized the available in-house laser system at the coherent x-ray imaging beamline at the Linac Coherent Light Source. Chirped laser pulses, i. e., the direct output from the regenerative amplifier of the Ti:Sa chirped pulse amplification laser system, were used to strongly align 2,5-diiodothiophene molecules in a molecular beam. The alignment laser pulses had pulse energies of a few mJ and a pulse duration of 94 ps. A degree of alignment of \left = 0.85 was measured, limited by the intrinsic temperature of the molecular beam rather than by the available laser system. With the general availability of synchronized chirped-pulse-amplified near-infrared laser systems at short-wavelength laser facilities, our approach allows for the universal preparation of molecules tightly fixed in space for experiments with x-ray pulses.Comment: 10 pages, 5 figure

Impact of opioid substitution therapy on antiretroviral therapy outcomes:a systematic review and meta-analysis

BACKGROUND: Human immunodeficiency virus (HIV)-infected people who inject drugs (PWID) frequently encounter barriers accessing and remaining on antiretroviral therapy (ART). Some studies have suggested that opioid substitution therapy (OST) could facilitate PWID's engagement with HIV services. We conducted a systematic review and meta-analysis to evaluate the impact of concurrent OST use on ART-related outcomes among HIV-infected PWID. METHODS: We searched Medline, PsycInfo, Embase, Global Health, Cochrane, Web of Science, and Social Policy and Practice databases for studies between 1996 to November 2014 documenting the impact of OST, compared to no OST, on ART outcomes. Outcomes considered were coverage and recruitment onto ART, adherence, viral suppression, attrition from ART, and mortality. Meta-analyses were conducted using random-effects modeling, and heterogeneity assessed using Cochran Q test and I2 statistic. RESULTS: We identified 4685 articles, and 32 studies conducted in North America, Europe, Indonesia, and China were included. OST was associated with a 69% increase in recruitment onto ART (hazard ratio [HR], 1.69; 95% confidence interval [CI], 1.32-2.15), a 54% increase in ART coverage (odds ratio [OR], 1.54; 95% CI, 1.17-2.03), a 2-fold increase in adherence (OR, 2.14; 95% CI, 1.41-3.26), and a 23% decrease in the odds of attrition (OR, 0.77; 95% CI, .63-.95). OST was associated with a 45% increase in odds of viral suppression (OR, 1.45; 95% CI, 1.21-1.73), but there was limited evidence from 6 studies for OST decreasing mortality for PWID on ART (HR, 0.91; 95% CI, .65-1.25). CONCLUSIONS: These findings support the use of OST, and its integration with HIV services, to improve the HIV treatment and care continuum among HIV-infected PWID

Confronting experimental data with heavy-ion models: Rivet for heavy ions

The Rivet library is an important toolkit in particle physics, and serves as a repository for analysis data and code. It allows for comparisons between data and theoretical calculations of the final state of collision events. This paper outlines several recent additions and improvements to the framework to include support for analysis of heavy ion collision simulated data. The paper also presents examples of these recent developments and their applicability in implementing concrete physics analyses

To Explain or Not to Explain?—Artificial Intelligence Explainability in Clinical Decision Support Systems

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice