Statistics of orderings

In this paper, we study a Ramsey type problems dealing with the number of ordered subgraphs present in an arbitrary ordering of a larger graph. Our first result implies that for every vertex ordered graph G on k vertices and any stochastic vector \(\overrightarrow{a}\) with k! entries, there exists a graph H with the following property: for any linear order of the vertices of H, the number of induced ordered copies of G in H is asymptotically equal to a convex combination of the entries in \(\overrightarrow{a}\). This for a particular choice of \(\overrightarrow{a}\) yeilds an earlier result of Angel, Lyons, and Kechris. We also consider a similar question when the ordering of vertices is replaced by the ordering of pairs of vertices. This problem is more complex problem and we prove some partial results in this case.     

Global Existence of a Weak Solution for a Model in Radiation Magnetohydrodynamics

We consider a simplified model based on the Navier-Stokes-Fourier system coupled to a transport equation and the Maxwell system, proposed to describe radiative flows in stars. We establish global-in-time existence for the associated initial-boundary value problem in the framework of weak solutions.     

Enhanced Raman Scattering from Vibro‐Polariton Hybrid States

Ground‐state molecular vibrations can be hybridized through strong coupling with the vacuum field of a cavity optical mode in the infrared region, leading to the formation of two new coherent vibro‐polariton states. The spontaneous Raman scattering from such hybridized light–matter states was studied, showing that the collective Rabi splitting occurs at the level of a single selected bond. Moreover, the coherent nature of the vibro‐polariton states boosts the Raman scattering cross‐section by two to three orders of magnitude, revealing a new enhancement mechanism as a result of vibrational strong coupling. This observation has fundamental consequences for the understanding of light‐molecule strong coupling and for molecular science.     

Multidimensional Design of Anticancer Peptides

The computer‐assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell‐penetrating peptides, and tumor‐homing peptides. Machine‐learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer‐generated sequences featured improved cancer‐cell penetration, induced cancer‐cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co‐administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine‐learning methods for rapidly obtaining peptides with the desired cellular activities.     

Mesoscale Characterization of Nanoparticles Distribution Using X‐ray Scattering

The properties of many functional materials depend critically on the spatial distribution of an active phase within a support. In the case of solid catalysts, controlling the spatial distribution of metal (oxide) nanoparticles at the mesoscopic scale offers new strategies to tune their performance and enhance their lifetimes. However, such advanced control requires suitable characterization methods, which are currently scarce. Here, we show how the background in small‐angle X‐ray scattering patterns can be analyzed to quantitatively access the mesoscale distribution of nanoparticles within supports displaying hierarchical porosity. This is illustrated for copper catalysts supported on meso‐ and microporous silica displaying distinctly different metal distributions. Results derived from X‐ray scattering are in excellent agreement with electron tomography. Our strategy opens unprecedented prospects for understanding the properties and to guide the synthesis of a wide array of functional nanomaterials.     

An Atomic‐Scale View of CO and H2 Oxidation on a Pt/Fe3O4 Model Catalyst

Metal–support interactions are frequently invoked to explain the enhanced catalytic activity of metal nanoparticles dispersed over reducible metal oxide supports, yet the atomic‐scale mechanisms are rarely known. In this report, scanning tunneling microscopy was used to study a Pt_1‐6/Fe₃O₄ model catalyst exposed to CO, H₂, O₂, and mixtures thereof at 550 K. CO extracts lattice oxygen atoms at the cluster perimeter to form CO₂, creating large holes in the metal oxide surface. H₂ and O₂ dissociate on the metal clusters and spill over onto the support. The former creates surface hydroxy groups, which react with the support, ultimately leading to the desorption of water, while oxygen atoms react with Fe from the bulk to create new Fe₃O₄(001) islands. The presence of the Pt is crucial because it catalyzes reactions that already occur on the bare iron oxide surface, but only at higher temperatures.     

De Novo Fragment Design for Drug Discovery and Chemical Biology

Automated molecular de novo design led to the discovery of an innovative inhibitor of death‐associated protein kinase 3 (DAPK3). An unprecedented crystal structure of the inactive DAPK3 homodimer shows the fragment‐like hit bound to the ATP pocket. Target prediction software based on machine learning models correctly identified additional macromolecular targets of the computationally designed compound and the structurally related marketed drug azosemide. The study validates computational de novo design as a prime method for generating chemical probes and starting points for drug discovery.     

Effect of Dimethyl Carbonate on the Dynamic Properties and Ionicities of Ionic Liquids with [MIII(hfip)4]− (M=B,Al) Anions

Several ionic liquids (ILs) comprising [B(hfip)₄]^? [hfip=OCH(CF₃)₂] or [Al(hfip)₄]^? anions and imidazolium or ammonium cations were prepared and mixed with up to 270 mol % of dimethyl carbonate (DMC). The viscosities, conductivities, and self‐diffusion constants of these mixtures and, where possible, of the neat ILs were measured and compared with common [NTf₂]^? based ILs and their mixtures with DMC. A tremendous decrease of the viscosities and a likewise increase of the conductivities and diffusion constants can be achieved for all classes of ILs. However, the order of the conductivities is partially reversed in the diffusion data. This is probably due to the low dielectric constant of DMC and the, thus, favored ion pairing, as evidenced, for example, by the calculated ionicities. Altogether, our data show that the chemically robust, but high‐melting and more viscous [B(hfip)₄]^? ILs might be candidates for electrolytes when mixed with suitable molecular solvents.     

Self‐defining tree‐like classifiers for interpretation of Raman spectroscopic experiments

In this contribution, a technique is proposed to create a data‐driven interpretation of a given chemometric analysis of a Raman dataset. In real‐world applications, the chemometric analysis is fixed by some external measurement, for example, a legal standard, or a set of fixed goals. Thus, the exact chemometric work flow is fixed because of those goals. However, a further optimization, for example, of the measurement itself relies on an interpretation of the resulting chemometric analysis. For this purpose, a data‐driven analysis of the chemometric analysis itself has to be carried out. This contribution tries to achieve that goal by combining two methods. The first proposed technique is the calculation of the so‐called importance map, which allows the computation of the importance of every channel for a given model and a given dataset. This importance map is constructed after the complete result of an out‐of‐bag (OOB) validation and the decrease of accuracy by randomized channels. The second technique is the growing of the optimal decision tree based on the action of the model used for chemometric analysis. By this way, a clustering is achieved on which by binary classifiers, the optimal decision tree is grown. This tree can be interpreted as dividing the whole dataset into meta clusters. Combining these techniques, a new way of interpreting datasets based on the chosen model is proposed. This combination closes the gap between chemometric analysis and the need for interpretation. Copyright © 2016 John Wiley & Sons, Ltd.     

Semantic,syntactic, and phonological processing of written words in adult developmental dyslexic readers: an event-related brain potential study

Background

The present study used event-related brain potentials to investigate semantic, phonological and syntactic processes in adult German dyslexic and normal readers in a word reading task. Pairs of German words were presented one word at a time. Subjects had to perform a semantic judgment task (house – window; are they semantically related?), a rhyme judgment task (house – mouse; do they rhyme?) and a gender judgment task (das – Haus [the – house]; is the gender correct? [in German, house has a neutral gender: das Haus]).

Results

Normal readers responded faster compared to dyslexic readers in all three tasks. Onset latencies of the N400 component were delayed in dyslexic readers in the rhyme judgment and in the gender judgment task, but not in the semantic judgment task. N400 and the anterior negativity peak amplitudes did not differ between the two groups. However, the N400 persisted longer in the dyslexic group in the rhyme judgment and in the semantic judgment tasks.

Conclusion

These findings indicate that dyslexics are phonologically impaired (delayed N400 in the rhyme judgment task) but that they also have difficulties in other, non-phonological aspects of reading (longer response times, longer persistence of the N400). Specifically, semantic and syntactic integration seem to require more effort for dyslexic readers and take longer irrespective of the reading task that has to be performed.