The performance comparison for different data sources.

<p>The best predictions are highlighted in bold.</p

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

<div><p>Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (<b>Pre</b>dict <b>D</b>rug <b>R</b>epositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.</p></div

Size-Dependent Reaction Pathways of Low-Temperature CO Oxidation on Au/CeO₂ Catalysts

Via a comprehensive time-resolved operando-DRIFTS study of the evolutions of various surface species on Au/CeO₂ catalysts with Au particle sizes ranging from 1.7 ± 0.6 to 3.7 ± 0.9 nm during CO oxidation at room temperature, we have successfully demonstrated size-dependent reaction pathways and their contributions to the catalytic activity. The types and concentrations of chemisorbed CO­(a), carbonate, bicarbonate, and formate species formed upon CO adsorption, their intrinsic oxidation/decomposition reactivity, and roles in CO oxidation vary with the size of the supported Au particles. The intrinsic oxidation reactivity of CO­(a) does not depend much on the Au particle size, whereas the intrinsic decomposition reactivity of carbonate, bicarbonate, and formate species strongly depend on the Au particle size and are facilitated over Au/CeO₂ catalysts with large Au particles. These results greatly advance the fundamental understanding of the size effect of Au/CeO₂ catalysts for low-temperature CO oxidation

The performance of predictions are shown as ROC curves.

<p>Subfigure A: The ROC curves for three data sources (“Chem”: chemical structure, “Inter”: target protein, “Side-effect”: side-effect based similarity and “Comb”: integration of “Chem”, “Inter”, and “Side-effect”). “Side-effect” is general more predictive for more experimentally observed drug-disease associations. Subfigure B shows the ROC curves with false positive rate (FPR) less than 0.05. “Chem” obtains the highest true positive rate (TPR) when FPR is very small.</p

The predicted drug-disease network (only top 100 novel predictions are shown).

<p>LightCoral rectangle represents drug and LightSteelBlue cycle represents disease. Pink solid line represents the known interaction and the DarkBlue dash line represents the new prediction.</p

Pathway ‘Arachidonic Acid metabolism’.

<p>Drug target proteins and disease genes are highlighted by orange border.</p

Leave one drug out cross-validation.

<p>Subfigure A: The procedure for leave one drug out cross-validation. Subfigure B: The AUCs obtained from leave one drug out cross-validation (“Chem”: chemical structure, “Inter”: target protein, “Side-effect”: side-effect, and “Comb”: integration of “Chem”, “Inter”, and “Side-effect”). It further shows that all three data sources can uncover new diseases for a novel drug, and integration works even better.</p

Highly Stable and Sensitive Paper-Based Bending Sensor Using Silver Nanowires/Layered Double Hydroxides Hybrids

Highly sensitive flexible piezoresistive materials using silver nanowires (AgNWs) composites have been widely researched due to their excellent electrical, optical, and mechanical properties. Intrinsically, AgNWs tend to aggregate in polymer matrix because of the intense depletion-induced interactions, which seriously influence the percolation threshold of the composites. In this study, we report a highly stable and sensitive paper-based bending sensor using the AgNWs and layered double hydroxides (LDHs) to construct a hybrid conductive network in waterborne polyurethane that is easy to destruct and reconstruct under bending deformation. The nonconductive 2D LDH nanosheets are embedded into AgNWs network and assist dispersion of AgNWs, which depends on the hydrogen bonding between the two nanostructures. The percolation threshold of the composites decreases from 10.8 vol % (55 wt %) to 3.1 vol % (23.8 wt %), and the composites reaches a very low resistivity (10^–4 Ω·cm) with a small amount of AgNWs (8.3 vol %) due to the dispersion improvement of AgNWs with the effect of LDH nanosheets. The as-prepared conductive composites with low percolation threshold can be manufactured on paper via various methods such as rollerball pen writing, inkjet printing, or screen printing. The bending sensor prepared by manufacturing the composites on paper shows low-cost, excellent conductivity, flexibility (>3000 bending cycles), sensitivity (0.16 rad^–1), fast response (120 ms) and relaxation time (105 ms), and nontoxicity. Therefore, a simple but efficient wearable sensor is developed to monitor the human motions (such as fingers and elbow joints movements) and presents good repeatability, stability, and responsiveness, making the bending sensor possibly able to meet the needs in numerous applications for robotic systems

Probing Surface Structures of CeO₂, TiO₂, and Cu₂O Nanocrystals with CO and CO₂ Chemisorption

CO and CO₂ chemisorption on uniform CeO₂, TiO₂, and Cu₂O nanocrystals with various morphologies were comprehensively studied with in-situ diffuse reflectance infrared Fourier transform spectroscopy. The formed adsorbates were observed to be morphology dependent. CO or CO₂ chemisorbed at the metal cation sites, and bidentate and bridged carbonates involving the O sites are sensitive to the surface composition and the local coordination environments of surface metal cations and O anions and can be correlated well with the surface structures of facets exposed on oxide nanocrystals. Carbonate and carbonite species formed by CO chemisorption can probe the different facets of CeO₂. Carbonate species formed by CO chemisorption can probe the different facets of TiO₂. Adsorbed CO and carbonate species formed by CO chemisorption can probe the different facets of Cu₂O, and adsorbed CO₂ formed by CO₂ chemisorption can also probe the different facets of Cu₂O. These results demonstrate chemisorption of probing molecules as a convenient technique to identify surface structures of different facets of oxide nanocrystals and lay the foundations of surface structures for the fundamental understanding of catalysis and other surface-mediated functions of CeO₂, TiO₂, and Cu₂O nanocrystals