Following initial declines, in mid 2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the US and Europe. As COVID19 disease control efforts are re-intensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the US and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20-49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one, and that at least 65 of 100 COVID-19 infections originate from individuals aged 20-49 in the US. Targeting interventions – including transmission-blocking vaccines – to adults aged 20-49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.

The normal‐normal hierarchical model (NNHM) constitutes a simple and widely used framework for meta‐analysis. In the common case of only few studies contributing to the meta‐analysis, standard approaches to inference tend to perform poorly, and Bayesian meta‐analysis has been suggested as a potential solution. The Bayesian approach, however, requires the sensible specification of prior distributions. While noninformative priors are commonly used for the overall mean effect, the use of weakly informative priors has been suggested for the heterogeneity parameter, in particular in the setting of (very) few studies. To date, however, a consensus on how to generally specify a weakly informative heterogeneity prior is lacking. Here we investigate the problem more closely and provide some guidance on prior specification.

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Randomized controlled trials are the gold standard to investigate efficacy and safety of new treatments. In certain settings, however, randomizing patients to control may be difficult for ethical or feasibility reasons. Borrowing strength using relevant individual patient data on control from external trials or real‐world data (RWD) sources may then allow us to reduce, or even eliminate, the concurrent control group. Naive direct use of external control data is not valid due to differences in patient characteristics and other confounding factors. Instead, we suggest the rigorous application of meta‐analytic and propensity score methods to use external controls in a principled way. We illustrate these methods with two case studies: (i) a single‐arm trial in a rare cancer disease, using propensity score matching to construct an external control from RWD; (ii) a randomized trial in children with multiple sclerosis, borrowing strength from past trials using a Bayesian meta‐analytic approach.

Determining the sample size of an experiment can be challenging, even more so when incorporating external information via a prior distribution. Such information is increasingly used to reduce the size of the control group in randomized clinical trials. Knowing the amount of prior information, expressed as an equivalent prior effective sample size (ESS), clearly facilitates trial designs. Various methods to obtain a prior's ESS have been proposed recently. They have been justified by the fact that they give the standard ESS for one-parameter exponential families. However, despite being based on similar information-based metrics, they may lead to surprisingly different ESS for non-conjugate settings, which complicates many designs with prior information. We show that current methods fail a basic predictive consistency criterion, which requires the expected posterior-predictive ESS for a sample of size N to be the sum of the prior ESS and N . The expected local-information-ratio ESS is introduced and shown to be predictively consistent. It corrects the ESS of current methods, as shown for normally distributed data with a heavy-tailed Student-t prior and exponential data with a generalized Gamma prior. Finally, two applications are discussed: the prior ESS for the control group derived from historical data, and the posterior ESS for hierarchical subgroup analyses.

Zintl phases form hydrides either by incorporating hydride anions (interstitial hydrides) or by covalent bonding of H to the polyanion (polyanionic hydrides), which yields a variety of different compositions and bonding situations. Hydrides (deuterides) of SrGe, BaSi, and BaSn were prepared by hydrogenation (deuteration) of the CrB-type Zintl phases AeTt and characterized by laboratory X-ray, synchrotron, and neutron diffraction, NMR spectroscopy, and quantum-chemical calculations. SrGeD and BaSnD show condensed boatlike six-membered rings of Tt atoms, formed by joining three of the zigzag chains contained in the Zintl phase. These new polyanionic motifs are terminated by covalently bound H atoms with d(Ge-D) = 1.521(9) Å and d(Sn-D) = 1.858(8) Å. Additional hydride anions are located in Ae tetrahedra; thus, the features of both interstitial hydrides and polyanionic hydrides are represented. BaSiD retains the zigzag Si chain as in the parent Zintl phase, but in the hydride (deuteride), it is terminated by H (D) atoms, thus forming a linear (SiD) chain with d(Si-D) = 1.641(5) Å.

Bimetallic Ni-Fe catalysts show great potential for CO2 methanation concerning activity, selectivity and long-term stability even under transient reaction conditions as required for Power-to-X applications. Various contrary suggestions on the...

CO2 methanation is often performed on Ni/Al2O3 catalysts, which can suffer from mass transport limitations and, therefore, decreased efficiency. Here we show the application of a hierarchically porous Ni/Al2O3 catalyst for methanation of CO2. The material has a well-defined and connected meso- and macropore structure with a total porosity of 78%. The pore structure was thoroughly studied with conventional methods, i.e., N2 sorption, Hg porosimetry, and He pycnometry, and advanced imaging techniques, i.e., electron tomography and ptychographic X-ray computed tomography. Tomography can quantify the pore system in a manner that is not possible using conventional porosimetry. Macrokinetic simulations were performed based on the measures obtained by porosity analysis. These show the potential benefit of enhanced mass-transfer properties of the hierarchical pore system compared to a pure mesoporous catalyst at industrially relevant conditions. Besides the investigation of the pore system, the catalyst was studied by Rietveld refinement, diffuse reflectance ultraviolet-visible (DRUV/vis) spectroscopy, and H2-temperature programmed reduction (TPR), showing a high reduction temperature required for activation due to structural incorporation of Ni into the transition alumina. The reduced hierarchically porous Ni/Al2O3 catalyst is highly active in CO2 methanation, showing comparable conversion and selectivity for CH4 to an industrial reference catalyst.