We demonstrated that ultraviolet Raman spectroscopy is an effective technique to measure the transition temperature ( T c ) in ferroelectric ultrathin films and superlattices. We showed that one-unit-cell-thick BaTiO 3 layers in BaTiO 3 /SrTiO 3 superlattices are not only ferroelectric (with T c as high as 250 kelvin) but also polarize the quantum paraelectric SrTiO 3 layers adjacent to them. T c was tuned by ∼500 kelvin by varying the thicknesses of the BaTiO 3 and SrTiO 3 layers, revealing the essential roles of electrical and mechanical boundary conditions for nanoscale ferroelectricity.

The photopolymerization kinetics of typical dental dimethacrylate monomers were studied by differential photocalorimetry. Increasing proportions of the low-viscosity diluent monomer triethylene glycol dimethacrylate (TEGDMA) were added to either Bis-GMA (2,2-bis[p-(2‘-hydroxy-3‘-methacryloxypropoxy)phenylene]propane), EBADMA (ethoxylated bisphenol A dimethacrylate), or UDMA (1,6-bis(methacryloxy-2-ethoxycarbonylamino)-2,4,4-trimethylhexane) to provide three base resins that differed in their hydrogen-bonding potential and, therefore, resulted in compositions covering a broad range of viscosities. When compared at similar diluent concentrations, UDMA resins were significantly more reactive than Bis-GMA and EBADMA resins. At higher diluent concentrations, EBADMA resins provided the lowest photopolymerization reactivities. Optimum reactivities in the UDMA and EBADMA resin systems were obtained with the addition of relatively small amounts of TEGDMA, whereas the Bis-GMA/TEGDMA resin system required near equivalent mole ratios for highest reactivity. The hydrogen-bonding interactions, which substantially influence the Bis-GMA and UDMA resin series, were examined by Fourier transform infrared spectroscopy and resin viscosity. Synergistic effects of base and diluent monomer on the polymerization rate and the final conversion were found for the two base resins having hydrogen-bonding interactions. The structures of the individual monomers and, consequently, the resin viscosities of the comonomer mixtures strongly influence both the rate and the extent of conversion of the photopolymerization process.

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

The novel introduction of spaced seed idea in the filtration stage of sequence comparison by Ma et al. (Bioinformatics 18 (2002) 440) has greatly increased the sensitivity of homology search without compromising the speed of search. Finding the optimal spaced seeds is of great importance both theoretically and in designing better search tool for sequence comparison. In this paper, we study the computational aspects of calculating the hitting probability of spaced seeds; and based on these results, we propose an efficient algorithm for identifying optimal spaced seeds. r