Energetic Plasticizers Based on gem-Dinitrodiols

In this article, the syntheses and attempts at synthesis for the preparation of gem-dinitrodiols and their derivatives are outlined. The three gem-dinitrodiols described are 2,2-dinitropropane-1,3-diol, 3,3-dinitropentane-1,5-diol, and 4,4-dinitroheptane-1,7-diol. The nitrooxy, azido, and azidoacetoxy derivatives of the latter were prepared and are compared to derivatives of the first, which were presented in a previous article. The tedious and expensive synthesis of pentanediol precluded its use as an intermediate. Only one compound both had excellent glass transition temperature and was thermally stable. This compound was 4,4-dinitro-1,7-diazidoheptane.     

Effect of Structural Relaxation on the In-Plane Electrical Resistance of Oxygen-Underdoped ReBa_2Cu_3O_{7-\delta } (Re = Y,Ho) Single Crystals

The effect of jumpwise temperature variation and room-temperature storing on the basal-plane electrical resistivity $\rho _{ab}$ of underdoped ReBa $_2$ Cu $_3$ O $_{7-\delta }$ (Re = Y, Ho) single crystals is investigated. Reducing the oxygen content has been revealed to lead to the phase segregation accompanied by both, labile component diffusion and structural relaxation in the sample volume. Room-temperature storing of ${\text {YBa}}_2{\text {Cu}}_3{\text {O}}_{7-\delta }$ single crystals with different oxygen hypostoichiometries leads to a substantial widening of the rectilinear segment in $\rho _{ab}(T)$ in conjunction with a narrowing of the temperature range of existence of the pseudogap state. It is established that the excess conductivity obeys an exponential law in a broad temperature range, while the pseudogap’s temperature dependence is described satisfactory in the framework of the BCS-BEC crossover theory. Substituting yttrium with holmium essentially effects the charge distribution and the effective interaction in CuO planes, thereby stimulating disordering processes in the oxygen subsystem. This is accompanied by a notable shift of the temperature zones corresponding to transitions of the metal-insulator type and to the regime of manifestation of the pseudogap anomaly.     

Safe projections of binary data sets

Selectivity estimation of a boolean query based on frequent itemsets can be solved by describing the problem by a linear program. However, the number of variables in the equations is exponential, rendering the approach tractable only for small-dimensional cases. One natural approach would be to project the data to the variables occurring in the query. This can, however, change the outcome of the linear program.We introduce the concept of safe sets: projecting the data to a safe set does not change the outcome of the linear program. We characterise safe sets using graph theoretic concepts and give an algorithm for finding minimal safe sets containing given attributes. We describe a heuristic algorithm for finding almost-safe sets given a size restriction, and show empirically that these sets outperform the trivial projection.We also show a connection between safe sets and Markov Random Fields and use it to furtherreduce the number of variables in the linear program, given some regularity assumptions on the frequent itemsets.     

Deciding Effectively Propositional Logic Using DPLL and Substitution Sets

We introduce a DPLL calculus that is a decision procedure for the Bernays-Schönfinkel class, also known as EPR. Our calculus allows combining techniques for efficient propositional search with data-structures, such as Binary Decision Diagrams, that can efficiently and succinctly encode finite sets of substitutions and operations on these. In the calculus, clauses comprise of a sequence of literals together with a finite set of substitutions; truth assignments are also represented using substitution sets. The calculus works directly at the level of sets, and admits performing simultaneous constraint propagation and decisions, resulting in potentially exponential speedups over existing approaches.     

Uncovering the plot: detecting surprising coalitions of entities in multi-relational schemas

Many application domains such as intelligence analysis and cybersecurity require tools for the unsupervised identification of suspicious entities in multi-relational/network data. In particular, there is a need for automated semi-automated approaches to ‘uncover the plot’, i.e., to detect non-obvious coalitions of entities bridging many types of relations. We cast the problem of detecting such suspicious coalitions and their connections as one of mining surprisingly dense and well-connected chains of biclusters over multi-relational data. With this as our goal, we model data by the Maximum Entropy principle, such that in a statistically well-founded way we can gauge the surprisingness of a discovered bicluster chain with respect to what we already know. We design an algorithm for approximating the most informative multi-relational patterns, and provide strategies to incrementally organize discovered patterns into the background model. We illustrate how our method is adept at discovering the hidden plot in multiple synthetic and real-world intelligence analysis datasets. Our approach naturally generalizes traditional attribute-based maximum entropy models for single relations, and further supports iterative, human-in-the-loop, knowledge discovery.     

Induced chirality through electromagnetic coupling between chiral molecular layers and plasmonic nanostructures

We report a new approach for creating chiral plasmonic nanomaterials. A previously unconsidered, far-field mechanism is utilized which enables chirality to be conveyed from a surrounding chiral molecular material to a plasmonic resonance of an achiral metallic nanostructure. Our observations break a currently held preconception that optical properties of plasmonic particles can most effectively be manipulated by molecular materials through near-field effects. We show that far-field electromagnetic coupling between a localized plasmon of a nonchiral nanostructure and a surrounding chiral molecular layer can induce plasmonic chirality much more effectively (by a factor of 10(3)) than previously reported near-field phenomena. We gain insight into the mechanism by comparing our experimental results to a simple electromagnetic model which incorporates a plasmonic object coupled with a chiral molecular medium. Our work offers a new direction for the creation of hybrid molecular plasmonic nanomaterials that display significant chiroptical properties in the visible spectral region.     

Prediction of proprotein convertase cleavage sites

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.     

Feature-based prediction of non-classical and leaderless protein secretion

We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.