Influence of chain length and branching on poly(ADP-ribose)-protein interactions.

Hundreds of proteins interact with poly(ADP-ribose) (PAR) via multiple PAR interaction motifs, thereby regulating their physico-chemical properties, sub-cellular localizations, enzymatic activities, or protein stability. Here, we present a targeted approach based on fluorescence correlation spectroscopy (FCS) to characterize potential structure-specific interactions of PAR molecules of defined chain length and branching with three prime PAR-binding proteins, the tumor suppressor protein p53, histone H1, and the histone chaperone APLF. Our study reveals complex and structure-specific PAR-protein interactions. Quantitative Kd values were determined and binding affinities for all three proteins were shown to be in the nanomolar range. We report PAR chain length dependent binding of p53 and H1, yet chain length independent binding of APLF. For all three PAR binders, we found a preference for linear over hyperbranched PAR. Importantly, protein- and PAR-structure-specific binding modes were revealed. Thus, while the H1-PAR interaction occurred largely on a bi-molecular 1:1 basis, p53-and potentially also APLF-can form complex multivalent PAR-protein structures. In conclusion, our study gives detailed and quantitative insight into PAR-protein interactions in a solution-based setting at near physiological buffer conditions. The results support the notion of protein and PAR-structure-specific binding modes that have evolved to fit the purpose of the respective biochemical functions and biological contexts.

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*.

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys. This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

What Makes High-Entropy Alloys Exceptional Electrocatalysts?

The formation of a vast number of different multielement active sites in compositionally complex solid solution materials, often more generally termed high-entropy alloys, offers new and unique concepts in catalyst design, which mitigate existing limitations and change the view on structure-activity relations. We discuss these concepts by summarising the currently existing fundamental knowledge and critically assess the chances and limitations of this material class, also highlighting design strategies. A roadmap is proposed, illustrating which of the characteristic concepts could be exploited using which strategy, and which breakthroughs might be possible to guide future research in this highly promising material class for (electro)catalysis.

Complex-Solid-Solution Electrocatalyst Discovery by Computational Prediction and High-Throughput Experimentation*.

Complex solid solutions ("high entropy alloys"), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data-driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition-spread thin-film libraries are synthesised. High-throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru. The method can identify optimal complex-solid-solution materials for electrocatalytic reactions in an unprecedented manner.

Sputter deposition of highly active complex solid solution electrocatalysts into an ionic liquid library: effect of structure and composition on oxygen reduction activity.

Complex solid solution electrocatalysts (often called high-entropy alloys) present a new catalyst class with highly promising features due to the interplay of multi-element active sites. One hurdle is the limited knowledge about structure-activity correlations needed for targeted catalyst design. We prepared Cr-Mn-Fe-Co-Ni nanoparticles by magnetron sputtering a high entropy Cantor alloy target simultaneously into an ionic liquid library. The synthesized nanoparticles have a narrow size distribution but different sizes (from 1.3 ± 0.1 nm up to 2.6 ± 0.3 nm), different crystallinity (amorphous, face-centered cubic or body-centered cubic) and composition (i.e. high Mn versus low Mn content). The Cr-Mn-Fe-Co-Ni complex solid solution nanoparticles possess an unprecedented intrinsic electrocatalytic activity for the oxygen reduction reaction in alkaline media, some of them even surpassing that of Pt. The highest intrinsic activity was obtained for body-centered cubic nanoparticles with a low Mn and Fe content which were synthesized using the ionic liquid 1-etyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [Emimi][(Tf)2N].

Medical education in times of COVID-19: German students' expectations - A cross-sectional study.

BACKGROUND: Since the COVID-19 pandemic has affected the education of medical students, medical faculties have faced the challenge of adapting instruction to digital platforms. Although medical students are willing to support pandemic response efforts, how the crisis will affect their medical training remains uncertain. Thus, in this study, we investigated the teaching- and learning-related stressors and expectations of medical students in Germany during the COVID-19 pandemic. METHODS: A cross-sectional survey was distributed online to undergraduate medical students at medical faculties in Germany. Students answered questions about COVID-19 and teaching (on a 7-point Likert scale from 0 ("not at all") to 6 ("completely")) and completed mental well-being measurements, including the State-Trait Anxiety Inventory (STAI), the Generalised Anxiety Disorder scale (GAD-7) and the Perceived Health Questionnaire (PHQ-9). Descriptive data analysis, a t-test and Pearson correlations were performed to process the data. RESULTS: Medical students felt well-informed about COVID-19 in general (M = 5.64, SD = 1.28) and in the medical context (M = 5.14, SD = 1.34) but significantly less informed about the pandemic in the academic context, M = 2.47, SD = 1.49, t(371) = 31.98, p < .001. Their distress levels were high (STAI: M = 45.12, SD = 4.73) and significantly correlated with the academic context (rp = .164, p < .01) but not their private lives. Concerning how they were taught, they most often expected online lectures (91.7%) and live broadcasts (67.2%) and less often expected innovative digital teaching strategies, including serious games (17.3%) and virtual-reality exercises (16.7%). DISCUSSION: Medical students seem to be aware of the COVID-19 pandemic and its consequences for academic and healthcare contexts. They also seem to think that their teachers will enhance their digital competencies during the pandemic. Therefore, faculties of medicine need to rapidly and adequately digitalise their approaches to teaching.

Sex-specific features of spine densities in the hippocampus.

Previously, we found that in dissociated hippocampal cultures the proportion of large spines (head diameter ≥ 0.6 µm) was larger in cultures from female than from male animals. In order to rule out that this result is an in vitro phenomenon, we analyzed the density of large spines in fixed hippocampal vibratome sections of Thy1-GFP mice, in which GFP is expressed only in subpopulations of neurons. We compared spine numbers of the four estrus cycle stages in females with those of male mice. Remarkably, total spine numbers did not vary during the estrus cycle, while estrus cyclicity was evident regarding the number of large spines and was highest during diestrus, when estradiol levels start to rise. The average total spine number in females was identical with the spine number in male animals. The density of large spines, however, was significantly lower in male than in female animals in each stage of the estrus cycle. Interestingly, the number of spine apparatuses, a typical feature of large spines, did not differ between the sexes. Accordingly, NMDA-R1 and NMDA-R2A/B expression were lower in the hippocampus and in postsynaptic density fractions of adult male animals than in those of female animals. This difference could already be observed at birth for NMDA-R1, but not for NMDA-R2A/B expression. In dissociated embryonic hippocampal cultures, no difference was seen after 21 days in culture, while the difference was evident in postnatal cultures. Our data indicate that hippocampal neurons are differentiated in a sex-dependent manner, this differentiation being likely to develop during the perinatal period.

Design of Complex Solid-Solution Electrocatalysts by Correlating Configuration, Adsorption Energy Distribution Patterns, and Activity Curves.

Complex solid-solution electrocatalysts (also referred to as high-entropy alloy) are gaining increasing interest owing to their promising properties which were only recently discovered. With the capability of forming complex single-phase solid solutions from five or more constituents, they offer unique capabilities of fine-tuning adsorption energies. However, the elemental complexity within the crystal structure and its effect on electrocatalytic properties is poorly understood. We discuss how addition or replacement of elements affect the adsorption energy distribution pattern and how this impacts the shape and activity of catalytic response curves. We highlight the implications of these conceptual findings on improved screening of new catalyst configurations and illustrate this strategy based on the discovery and experimental evaluation of several highly active complex solid solution nanoparticle catalysts for the oxygen reduction reaction in alkaline media.

Controlling the Amorphous and Crystalline State of Multinary Alloy Nanoparticles in An Ionic Liquid.

Controlling the amorphous or crystalline state of multinary Cr-Mn-Fe-Co-Ni alloy nanoparticles with sizes in the range between ~1.7 nm and ~4.8 nm is achieved using three processing routes. Direct current sputtering from an alloy target in the ionic liquid 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide leads to amorphous nanoparticles as observed by high-resolution transmission electron microscopy. Crystalline nanoparticles can be achieved in situ in a transmission electron microscope by exposure to an electron beam, ex situ by heating in vacuum, or directly during synthesis by using a high-power impulse magnetron sputtering process. Growth of the nanoparticles with respect to the amorphous particles was observed. Furthermore, the crystal structure can be manipulated by the processing conditions. For example, a body-centered cubic structure is formed during in situ electron beam crystallization while longer ex situ annealing induces a face-centered cubic structure.

Evaluation of the intrinsic catalytic activity of nanoparticles without prior knowledge of the mass loading.

The quantitative characterisation of electrocatalytic properties of nanoparticle catalyst materials is so far only performed for layers typically comprising additionally conducting additives and binders. We propose a method enabling the evaluation of intrinsic catalytic activity of nanoparticles based on the diffusion-limited steady-state current. In a step-after-step process, the influence of coverage on kinetic and diffusion limited current is evaluated to highlight the challenges of sub-monolayer electroanalysis. Conclusions are used to point out strategies and their limitations for qualitative and quantitative comparison of intrinsic catalytic properties. Particularly, the impact of coverage, electrode geometry, altered diffusion profile for nanoparticles and the catalyst activity and selectivity are discussed. Fundamental information about electrochemical sub-monolayer nanoparticle analysis is provided.